DeMixT/DESCRIPTION0000644000175200017520000000232414710276061014441 0ustar00biocbuildbiocbuildPackage: DeMixT Title: Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms Version: 1.22.0 Date: 2022-10-04 Author: Zeya Wang , Shaolong Cao, Wenyi Wang Maintainer: Ruonan Li Description: DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components. LazyData: TRUE Depends: R (>= 3.6.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment, knitr, KernSmooth, matrixcalc, rmarkdown, DSS, dendextend, psych, sva Imports: matrixStats, stats, truncdist, base64enc, ggplot2 LinkingTo: Rcpp NeedsCompilation: yes VignetteBuilder: knitr biocViews: Software, StatisticalMethod, Classification, GeneExpression, Sequencing, Microarray, TissueMicroarray, Coverage License: GPL-3 RoxygenNote: 7.1.2 Packaged: 2024-10-30 00:22:09 UTC; biocbuild git_url: https://git.bioconductor.org/packages/DeMixT git_branch: RELEASE_3_20 git_last_commit: 71bb355 git_last_commit_date: 2024-10-29 Repository: Bioconductor 3.20 Date/Publication: 2024-10-29 DeMixT/DeMixT_Log.Rmd0000644000175200017520000001033314710233064015325 0ustar00biocbuildbiocbuild--- output: pdf_document: default html_notebook: default header-includes: \usepackage[ruled,vlined,linesnumbered]{algorithm2e} --- # DeMixT Log Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms. ## Update from version 1.10.1 to 1.20.1 Script DeMixT_preprocessing.R is included in the package for input data preprocessing. The DeMixT tutorial at https://wwylab.github.io/DeMixT/ is updated. ## Update from version 1.8.2 to 1.10.1 A DeMixT tutorial is included and available at https://wwylab.github.io/DeMixT/. README.md is revised. ## Update from version 1.8.1 to 1.8.2 Revised the link for citation. ## Update from version 1.6.0 to 1.8.1 Fixed error messenge when process vignette 'DeMixT.md' failed. Fixed typo in the documentation of function DeMixT, DeMixT_DE and DeMixT_GS. ## Update from version 1.4.0 to 1.6.0 Gene expression data of normal tissues (Lung, Prostate and Thyroid) from the GTEx study are included. Rename DeMixT_S1 function to DeMixT_DE. ## Update from version 1.2.5 to 1.4.0 Disabled OpenMP under R 4.0.0 for Mac OS platform. ## Update from version 1.2.4 to 1.2.5 Error appears if the number of genes is not mutiplied by 10. ````markdown ngene.Profile.selected<-min(1500, 0.3*nrow(data.Y)) ```` ````markdown ngene.Profile.selected<-min(1500, round(0.3*nrow(data.Y))) ```` ## Update from version 1.2.3 to 1.2.4 The step for computing MuN mis-specify the groupid, since MuN corresponds to goupdid == 1. ````markdown mun.obs<-rowMeans(log2(inputdata2[, groupid == 3]+0.001)) ```` ````markdown mun.obs<-rowMeans(log2(inputdata2[, groupid == 1]+0.001)) ```` ## Update from version 1.2.1 to 1.2.3 ## Major Changes 1. Added pi01 and pi02 as input values for users to initialize the proportion estimation. 2. Added nspikein as an input value in the DeMixT, DeMixT_S1 and DeMixT_GS functions to specify how many spike-in normal reference samples need to be generated; Setting nspikein at null as a default value, the number of spike-in normal reference samples equal the $min(200, 0.3\times My)$, where $My$ is the number of mixed samples; By setting nspikein equals 0, no spike-in normal reference will be generated; If the input value of data.N2 is not null, nspikein will be forced to be 0. 3. Added DeMixT_GS function, new proposed gene selection method which applies profile likelihood, for proportion estimation. 4. Added simulate_2comp function for users to simulate test data for 2-component de-convolution. 5. Added simulate_3comp function for users to simulate test data for 3-component de-convolution. 6. Added row names and column names for all output values. 7. Added gene.selection.method as an input value for DeMixT function. The default is 'GS'. 8. Added ngene.Profile.selected as an input value for DeMixT function. The default is NA. ## Bug fix The filter step failed to set the value into inputdata in the previous version, and it has been resolved in the 1.3.0 version. ````markdown ## filter out genes with constant value across all samples inputdata < ifelse(is.null(data.comp2), inputdata[apply(data.comp1, 1, function(x) length(unique(x)) > 1), ], inputdata[apply(data.comp1, 1, function(x) length(unique(x)) > 1) & apply(data.comp2, 1, function(x) length(unique(x)) > 1), ]) ```` ````markdown ## filter out genes with constant value across all samples if (is.null(data.comp2)) { if (dim(inputdata)[1] == 1) { inputdata <- t(as.matrix(inputdata[apply(data.comp1, 1, function(x) length(unique(x)) > 1), ])) } else { inputdata <- inputdata[apply(data.comp1, 1, function(x) length(unique(x)) > 1), ] } } else { if (dim(inputdata)[1] == 1) { inputdata <- t(as.matrix(inputdata[apply(data.comp1, 1, function(x) length(unique(x)) > 1) & apply(data.comp2, 1, function(x) length(unique(x)) > 1), ])) } else { inputdata <- inputdata[apply(data.comp1, 1, function(x) length(unique(x)) > 1) & apply(data.comp2, 1, function(x) length(unique(x)) > 1), ] } } ```` DeMixT/DeMixT_Log.pdf0000644000175200017520000035773514710233064015400 0ustar00biocbuildbiocbuild%PDF-1.4 % 1 0 obj <> endobj 3 0 obj <> endobj 5 0 obj <> endobj 21 0 obj <> stream xnݶ| Tls6ŅS wF!%:8u- g' 釯{S{=};߅r?~깁/; x~ԍ߿>|ݗѬB` MhzNu 4;i{Н篧_1I?o0᧺bsktw<Y2 n\0: 6~EG):az/{_tAB]լ+7S_߷2Hа7%-8Ttkl?->6齅Y8tu@DGi|T(@DLF*T^yDhPcg-784aKA2F8r#wؙ㟇 X -`040 ; DL۱%+%oCyOϽ\Mwd(y&(Fx5U0-ዐ0k5+q'$gFF & NCиcqN)6ROr@H ,R C>cЅ.a0Clai *ӥ`Q."5ML"F'O˜Ќ2!NJCt%hUb7Q.DKE}?u`\4_T?# [?]f<=@ʞ$# |f<=("ИD! 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b4651526cda9d892411d3ba5858b50fc *vignettes/Res_3comp/res.S1.csv 0f445d1e15db0b53b7615b3e84e02975 *vignettes/Res_3comp/res.S1.rds 80030e400e13ae45cf7c90e3ce164b5c *vignettes/Res_3comp/res.S1.txt e76fe2f53f9a3bdfb1223810b34289e8 *vignettes/demixt.Rmd 5be1aeb0c06d2e463eb8f019c173d6bf *vignettes/hierarchical_clustering.png acbfe2f0650eda16c307bcb4fdcd15f4 *vignettes/pairwise_correlation.png 88d122b72724a08ebf83830db9a34247 *vignettes/sd_expresion.png DeMixT/NAMESPACE0000644000175200017520000000147014710233064014147 0ustar00biocbuildbiocbuilduseDynLib(DeMixT, .registration = TRUE) export(DeMixT) export(DeMixT_DE) export(DeMixT_S2) export(DeMixT_GS) export(detect_suspicious_sample_by_hierarchical_clustering_2comp) export(plot_sd) export(subset_sd) export(subset_sd_gene_remaining) export(plot_dim) export(scale_normalization_75th_percentile) export(DeMixT_preprocessing) export(batch_correction) export(simulate_2comp) export(simulate_3comp) export(Optimum_KernelC) import(KernSmooth) import(rmarkdown) import(SummarizedExperiment) import(matrixcalc) import(knitr) import(parallel) import(Rcpp) import(ggplot2) import(base64enc) import(DSS) import(dendextend) import(psych) import(sva) importFrom("matrixStats", "rowSds") importFrom("stats", "quantile") importFrom(Rcpp, evalCpp) importFrom("stats", "qnorm", "rnorm", "sd", "runif", "rbinom", "IQR")DeMixT/R/0000755000175200017520000000000014710233064013127 5ustar00biocbuildbiocbuildDeMixT/R/DeMixT.R0000644000175200017520000004646514710233064014423 0ustar00biocbuildbiocbuild#' @title Deconvolution of heterogeneous tumor samples with two or three #' components using expression data from RNAseq or microarray platforms #' #'@description DeMixT is a software that performs deconvolution on transcriptome #'data from a mixture of two or three components. #' #' @param data.Y A SummarizedExperiment object of expression data from mixed #' tumor samples. It is a \eqn{G} by \eqn{My} matrix where \eqn{G} is the number #' of genes and \eqn{My} is the number of mixed samples. Samples with the same #' tissue type should be placed together in columns. #' @param data.N1 A SummarizedExperiment object of expression data #' from reference component 1 (e.g., normal). It is a \eqn{G} by \eqn{M1} matrix #' where \eqn{G} is the number of genes and \eqn{M1} is the number of samples #' for component 1. #' @param data.N2 A SummarizedExperiment object of expression data from #' additional reference samples. It is a \eqn{G} by \eqn{M2} matrix where #' \eqn{G} is the number of genes and \eqn{M2} is the number of samples for #' component 2. Component 2 is needed only for running a three-component model. #' @param niter The maximum number of iterations used in the algorithm of #' iterated conditional modes. A larger value better guarantees #' the convergence in estimation but increases the running time. The default is #' 10. #' @param nbin The number of bins used in numerical integration for computing #' complete likelihood. A larger value increases accuracy in estimation but #' increases the running time, especially in a three-component deconvolution #' problem. The default is 50. #' @param if.filter The logical flag indicating whether a predetermined filter #' rule is used to select genes for proportion estimation. The default is TRUE. #' @param filter.sd The cut-off for the standard deviation of lognormal #' distribution. Genes whose log transferred standard deviation smaller than #' the cut-off will be selected into the model. The default is 0.5. #' @param ngene.selected.for.pi The percentage or the number of genes used for #' proportion estimation. The difference between the expression levels from #' mixed tumor samples and the known component(s) are evaluated, and the most #' differential expressed genes are selected, which is called DE. It is enabled #' when if.filter = TRUE. The default is \eqn{min(1500, 0.3*G)}, where #' \eqn{G} is the number of genes. Users can also try using more genes, #' ranging from \eqn{0.3*G} to \eqn{0.5*G}, and evaluate the outcome. #' @param mean.diff.in.CM Threshold of expression difference for selecting genes #' in the component merging strategy. We merge three-component to two-component #' by selecting genes with similar expressions for the two known components. #' Genes with the mean differences less than the threshold will be selected for #' component merging. It is used in the three-component setting, and is enabled #' when if.filter = TRUE. The default is 0.25. #' @param nspikein The number of spikes in normal reference used for proportion #' estimation. The default value is \eqn{ min(200, 0.3*My)}, where #' \eqn{My} the number of mixed samples. If it is set to 0, proportion #' estimation is performed without any spike in normal reference. #' @param gene.selection.method The method of gene selection used for proportion #' estimation. The default method is 'GS', which applies a profile likelihood based #' method for gene selection. If it is set to 'DE', the most differential expressed #' genes are selected. #' @param ngene.Profile.selected The number of genes used for proportion #' estimation ranked by profile likelihood. The default is #' \eqn{min(1500,0.1*G)}, where \eqn{G} is the number of genes. #' This is enabled only when gene.selection.method is set to 'GS'. #' @param tol The convergence criterion. The default is 10^(-5). #' @param output.more.info The logical flag indicating whether to show the #' estimated proportions in each iteration in the output. #' @param pi01 Initialized proportion for first kown component. The default is #' \eqn{Null} and pi01 will be generated randomly from uniform distribution. #' @param pi02 Initialized proportion for second kown component. pi02 is needed #' only for running a three-component model. The default is \eqn{Null} and pi02 #' will be generated randomly from uniform distribution. #' @param nthread The number of threads used for deconvolution when OpenMP is #' available in the system. The default is the number of whole threads minus one. #' In our no-OpenMP version, it is set to 1. #' #' @return #' \item{pi}{A matrix of estimated proportion. First row and second row #' corresponds to the proportion estimate for the known components and unkown #' component respectively for two or three component settings, and each column #' corresponds to one sample.} #' \item{pi.iter}{Estimated proportions in each iteration. It is a \eqn{niter* #' My*p} array, where \eqn{p} is the number of components. This is #' enabled only when output.more.info = TRUE.} #' \item{ExprT}{A matrix of deconvolved expression profiles corresponding to #' T-component in mixed samples for a given subset of genes. Each row #' corresponds to one gene and each column corresponds to one sample.} #' \item{ExprN1}{A matrix of deconvolved expression profiles corresponding to #' N1-component in mixed samples for a given subset of genes. Each row #' corresponds to one gene and each column corresponds to one sample.} #' \item{ExprN2}{A matrix of deconvolved expression profiles corresponding to #' N2-component in mixed samples for a given subset of genes in a #' three-component setting. Each row corresponds to one gene and each #' column corresponds to one sample.} #' \item{Mu}{A matrix of estimated \eqn{Mu} of log2-normal distribution for #' both known (\eqn{MuN1, MuN2}) and unknown component (\eqn{MuT}). Each row #' corresponds to one gene.} #' \item{Sigma}{Estimated \eqn{Sigma} of log2-normal distribution for both #' known (\eqn{SigmaN1, SigmaN2}) and unknown component (\eqn{SigmaT}). Each #' row corresponds to one gene.} #' \item{gene.name}{The names of genes used in estimating the proportions. #' If no gene names are provided in the original data set, the genes will be #' automatically indexed.} #' #' @author Zeya Wang, Wenyi Wang #' #' @seealso http://bioinformatics.mdanderson.org/main/DeMixT #' #' @examples #' # Example 1: simulated two-component data by using GS(gene selection method) #' data(test.data.2comp) #' # res <- DeMixT(data.Y = test.data.2comp$data.Y, #' # data.N1 = test.data.2comp$data.N1, #' # data.N2 = NULL, nspikein = 50, #' # gene.selection.method = 'GS', #' # niter = 10, nbin = 50, if.filter = TRUE, #' # ngene.selected.for.pi = 150, #' # mean.diff.in.CM = 0.25, tol = 10^(-5)) #' # res$pi #' # head(res$ExprT, 3) #' # head(res$ExprN1, 3) #' # head(res$Mu, 3) #' # head(res$Sigma, 3) #' # #' # Example 2: simulated two-component data by using DE(gene selection method) #' # data(test.data.2comp) #' # res <- DeMixT(data.Y = test.data.2comp$data.Y, #' # data.N1 = test.data.2comp$data.N1, #' # data.N2 = NULL, nspikein = 50, g #' # ene.selection.method = 'DE', #' # niter = 10, nbin = 50, if.filter = TRUE, #' # ngene.selected.for.pi = 150, #' # mean.diff.in.CM = 0.25, tol = 10^(-5)) #' # #' # Example 3: three-component mixed cell line data applying #' # component merging strategy #' # data(test.data.3comp) #' # res <- DeMixT(data.Y = test.data.3comp$data.Y, #' # data.N1 = test.data.3comp$data.N1, #' # data.N2 = test.data.3comp$data.N2, #' # if.filter = TRUE) #' # #' # Example: convert a matrix into the SummarizedExperiment format #' # library(SummarizedExperiment) #' # example <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3, byrow = TRUE) #' # example.se <- SummarizedExperiment(assays = list(counts = example)) #' #' @references Wang Z, Cao S, Morris J S, et al. Transcriptome Deconvolution of #' Heterogeneous Tumor Samples with Immune Infiltration. iScience, 2018, 9: 451-460. #' #' @keywords DeMixT #' #' @export #' Deconvolution of heterogeneous tumor samples with two or three components #' using expression data from RNAseq or microarray platforms #' #' DeMixT is a software that performs deconvolution on transcriptome data from #' a mixture of two or three components. #' #' #' @param data.Y A SummarizedExperiment object of expression data from mixed #' tumor samples. It is a \eqn{G} by \eqn{My} matrix where \eqn{G} is the #' number of genes and \eqn{My} is the number of mixed samples. Samples with #' the same tissue type should be placed together in columns. #' @param data.N1 A SummarizedExperiment object of expression data from #' reference component 1 (e.g., normal). It is a \eqn{G} by \eqn{M1} matrix #' where \eqn{G} is the number of genes and \eqn{M1} is the number of samples #' for component 1. #' @param data.N2 A SummarizedExperiment object of expression data from #' additional reference samples. It is a \eqn{G} by \eqn{M2} matrix where #' \eqn{G} is the number of genes and \eqn{M2} is the number of samples for #' component 2. Component 2 is needed only for running a three-component model. #' @param niter The maximum number of iterations used in the algorithm of #' iterated conditional modes. A larger value better guarantees the convergence #' in estimation but increases the running time. The default is 10. #' @param nbin The number of bins used in numerical integration for computing #' complete likelihood. A larger value increases accuracy in estimation but #' increases the running time, especially in a three-component deconvolution #' problem. The default is 50. #' @param if.filter The logical flag indicating whether a predetermined filter #' rule is used to select genes for proportion estimation. The default is TRUE. #' @param filter.sd The cut-off for the standard deviation of lognormal #' distribution. Genes whose log transferred standard deviation smaller than #' the cut-off will be selected into the model. The default is 0.5. #' @param ngene.selected.for.pi The percentage or the number of genes used for #' proportion estimation. The difference between the expression levels from #' mixed tumor samples and the known component(s) are evaluated, and the most #' differential expressed genes are selected, which is called DE. It is enabled #' when if.filter = TRUE. The default is \eqn{min(1500, 0.3*G)}, where \eqn{G} #' is the number of genes. Users can also try using more genes, ranging from #' \eqn{0.3*G} to \eqn{0.5*G}, and evaluate the outcome. #' @param mean.diff.in.CM Threshold of expression difference for selecting #' genes in the component merging strategy. We merge three-component to #' two-component by selecting genes with similar expressions for the two known #' components. Genes with the mean differences less than the threshold will be #' selected for component merging. It is used in the three-component setting, #' and is enabled when if.filter = TRUE. The default is 0.25. #' @param nspikein The number of spikes in normal reference used for proportion #' estimation. The default value is \eqn{ min(200, 0.3*My)}, where \eqn{My} the #' number of mixed samples. If it is set to 0, proportion estimation is #' performed without any spike in normal reference. #' @param gene.selection.method The method of gene selection used for #' proportion estimation. The default method is 'GS', which applies a profile #' likelihood based method for gene selection. If it is set to 'DE', the most #' differential expressed genes are selected. #' @param ngene.Profile.selected The number of genes used for proportion #' estimation ranked by profile likelihood. The default is #' \eqn{min(1500,0.1*G)}, where \eqn{G} is the number of genes. This is #' enabled only when gene.selection.method is set to 'GS'. #' @param tol The convergence criterion. The default is 10^(-5). #' @param output.more.info The logical flag indicating whether to show the #' estimated proportions in each iteration in the output. #' @param pi01 Initialized proportion for first kown component. The default is #' \eqn{Null} and pi01 will be generated randomly from uniform distribution. #' @param pi02 Initialized proportion for second kown component. pi02 is needed #' only for running a three-component model. The default is \eqn{Null} and pi02 #' will be generated randomly from uniform distribution. #' @param nthread The number of threads used for deconvolution when OpenMP is #' available in the system. The default is the number of whole threads minus #' one. In our no-OpenMP version, it is set to 1. #' @return \item{pi}{A matrix of estimated proportion. First row and second row #' corresponds to the proportion estimate for the known components and unkown #' component respectively for two or three component settings, and each column #' corresponds to one sample.} \item{pi.iter}{Estimated proportions in each #' iteration. It is a \eqn{niter* My*p} array, where \eqn{p} is the number of #' components. This is enabled only when output.more.info = TRUE.} #' \item{ExprT}{A matrix of deconvolved expression profiles corresponding to #' T-component in mixed samples for a given subset of genes. Each row #' corresponds to one gene and each column corresponds to one sample.} #' \item{ExprN1}{A matrix of deconvolved expression profiles corresponding to #' N1-component in mixed samples for a given subset of genes. Each row #' corresponds to one gene and each column corresponds to one sample.} #' \item{ExprN2}{A matrix of deconvolved expression profiles corresponding to #' N2-component in mixed samples for a given subset of genes in a #' three-component setting. Each row corresponds to one gene and each column #' corresponds to one sample.} \item{Mu}{A matrix of estimated \eqn{Mu} of #' log2-normal distribution for both known (\eqn{MuN1, MuN2}) and unknown #' component (\eqn{MuT}). Each row corresponds to one gene.} #' \item{Sigma}{Estimated \eqn{Sigma} of log2-normal distribution for both #' known (\eqn{SigmaN1, SigmaN2}) and unknown component (\eqn{SigmaT}). Each #' row corresponds to one gene.} \item{gene.name}{The names of genes used in #' estimating the proportions. If no gene names are provided in the original #' data set, the genes will be automatically indexed.} #' @author Zeya Wang, Wenyi Wang #' @seealso http://bioinformatics.mdanderson.org/main/DeMixT #' @references Wang Z, Cao S, Morris J S, et al. Transcriptome Deconvolution of #' Heterogeneous Tumor Samples with Immune Infiltration. iScience, 2018, 9: #' 451-460. #' @keywords DeMixT #' @examples #' #' # Example 1: simulated two-component data by using GS(gene selection method) #' data(test.data.2comp) #' # res <- DeMixT(data.Y = test.data.2comp$data.Y, #' # data.N1 = test.data.2comp$data.N1, #' # data.N2 = NULL, nspikein = 50, #' # gene.selection.method = 'GS', #' # niter = 10, nbin = 50, if.filter = TRUE, #' # ngene.selected.for.pi = 150, #' # mean.diff.in.CM = 0.25, tol = 10^(-5)) #' # res$pi #' # head(res$ExprT, 3) #' # head(res$ExprN1, 3) #' # head(res$Mu, 3) #' # head(res$Sigma, 3) #' # #' # Example 2: simulated two-component data by using DE(gene selection method) #' # data(test.data.2comp) #' # res <- DeMixT(data.Y = test.data.2comp$data.Y, #' # data.N1 = test.data.2comp$data.N1, #' # data.N2 = NULL, nspikein = 50, g #' # ene.selection.method = 'DE', #' # niter = 10, nbin = 50, if.filter = TRUE, #' # ngene.selected.for.pi = 150, #' # mean.diff.in.CM = 0.25, tol = 10^(-5)) #' # #' # Example 3: three-component mixed cell line data applying #' # component merging strategy #' # data(test.data.3comp) #' # res <- DeMixT(data.Y = test.data.3comp$data.Y, #' # data.N1 = test.data.3comp$data.N1, #' # data.N2 = test.data.3comp$data.N2, #' # if.filter = TRUE) #' # #' # Example: convert a matrix into the SummarizedExperiment format #' # library(SummarizedExperiment) #' # example <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3, byrow = TRUE) #' # example.se <- SummarizedExperiment(assays = list(counts = example)) #' #' #' @export DeMixT DeMixT <- function( data.Y, data.N1, data.N2 = NULL, niter = 10, nbin = 50, if.filter = TRUE, filter.sd = 0.5, ngene.selected.for.pi = NA, mean.diff.in.CM = 0.25, nspikein = min(200, ceiling(ncol(data.Y)*0.3)), gene.selection.method = 'GS', ngene.Profile.selected = NA, tol = 10^(-5), output.more.info = FALSE, pi01 = NULL, pi02 = NULL, nthread = parallel::detectCores() - 1) { message("Step 1: Estimation of Proportions\n") if (!is.null(data.N2)) nspikein = 0 if (gene.selection.method == 'DE'){ res.pi <- DeMixT_DE(data.Y = data.Y, data.N1 = data.N1, data.N2 = data.N2, niter = niter, nbin = nbin, if.filter = if.filter, filter.sd= filter.sd, nspikein = nspikein, ngene.selected.for.pi = ngene.selected.for.pi, mean.diff.in.CM = mean.diff.in.CM, tol = tol, pi01 = pi01, pi02 = pi02, nthread = nthread) }else{ res.pi <- DeMixT_GS(data.Y = data.Y, data.N1 = data.N1, data.N2 = data.N2, niter = niter, nbin = nbin, if.filter = if.filter, filter.sd = filter.sd, ngene.Profile.selected = ngene.Profile.selected, ngene.selected.for.pi = ngene.selected.for.pi, mean.diff.in.CM = mean.diff.in.CM, nspikein = nspikein, tol = tol, pi01 = pi01, pi02 = pi02, nthread = parallel::detectCores() - 1) } message("Step 2: Deconvolution of Expressions\n") res.S2 <- DeMixT_S2(data.Y = data.Y, data.N1 = data.N1, data.N2 = data.N2, givenpi = c(t(res.pi$pi[-nrow(res.pi$pi),])), nbin = nbin, nthread = nthread) message("Deconvolution is finished\n") if (is.null(data.N2)) { # two-component if (output.more.info) { return(list(pi = res.pi$pi, ExprT = res.S2$decovExprT, ExprN1 = res.S2$decovExprN1, Mu = res.S2$decovMu, Sigma = res.S2$decovSigma, pi.iter = res.pi$pi.iter, gene.name = res.pi$gene.name)) } return(list(pi = res.pi$pi, ExprT = res.S2$decovExprT, ExprN1 = res.S2$decovExprN1, Mu = res.S2$decovMu, Sigma = res.S2$decovSigma, gene.name = res.pi$gene.name)) } else { # three-component if (output.more.info) { return(list(pi = res.pi$pi, ExprT = res.S2$decovExprT, ExprN1 = res.S2$decovExprN1, ExprN2 = res.S2$decovExprN2, Mu = res.S2$decovMu, Sigma = res.S2$decovSigma, pi.iter = res.pi$pi.iter, gene.name = res.pi$gene.name)) } return(list(pi = res.pi$pi, ExprT = res.S2$decovExprT, ExprN1 = res.S2$decovExprN1, ExprN2 = res.S2$decovExprN2, Mu = res.S2$decovMu, Sigma = res.S2$decovSigma, gene.name = res.pi$gene.name)) } } DeMixT/R/DeMixT_DE.R0000644000175200017520000004214514710233064014762 0ustar00biocbuildbiocbuild#' Estimates the proportions of mixed samples for each mixing component #' #' This function is designed to estimate the deconvolved expressions of #' individual mixed tumor samples for unknown component for each gene. #' #' #' @param data.Y A SummarizedExperiment object of expression data from mixed #' tumor samples. It is a \eqn{G} by \eqn{My} matrix where \eqn{G} is the #' number of genes and \eqn{My} is the number of mixed samples. Samples with #' the same tissue type should be placed together in columns. #' @param data.N1 A SummarizedExperiment object of expression data from #' reference component 1 (e.g., normal). It is a \eqn{G} by \eqn{M1} matrix #' where \eqn{G} is the number of genes and \eqn{M1} is the number of samples #' for component 1. #' @param data.N2 A SummarizedExperiment object of expression data from #' additional reference samples. It is a \eqn{G} by \eqn{M2} matrix where #' \eqn{G} is the number of genes and \eqn{M2} is the number of samples for #' component 2. Component 2 is needed only for running a three-component model. #' @param niter The maximum number of iterations used in the algorithm of #' iterated conditional modes. A larger value better guarantees the convergence #' in estimation but increases the running time. The default is 10. #' @param nbin The number of bins used in numerical integration for computing #' complete likelihood. A larger value increases accuracy in estimation but #' increases the running time, especially in a three-component deconvolution #' problem. The default is 50. #' @param if.filter The logical flag indicating whether a predetermined filter #' rule is used to select genes for proportion estimation. The default is TRUE. #' @param filter.sd The cut-off for the standard deviation of lognormal #' distribution. Genes whose log transferred standard deviation smaller than #' the cut-off will be selected into the model. The default is 0.5. #' @param ngene.selected.for.pi The percentage or the number of genes used for #' proportion estimation. The difference between the expression levels from #' mixed tumor samples and the known component(s) are evaluated, and the most #' differential expressed genes are selected, which is called DE. It is enabled #' when if.filter = TRUE. The default is \eqn{min(1500, 0.3*G)}, where \eqn{G} #' is the number of genes. Users can also try using more genes, ranging from #' \eqn{0.3*G} to \eqn{0.5*G}, and evaluate the outcome. #' @param nspikein The number of spikes in normal reference used for proportion #' estimation. The default value is \eqn{ min(200, 0.3*My)}, where \eqn{My} the #' number of mixed samples. If it is set to 0, proportion estimation is #' performed without any spike in normal reference. #' @param mean.diff.in.CM Threshold of expression difference for selecting #' genes in the component merging strategy. We merge three-component to #' two-component by selecting genes with similar expressions for the two known #' components. Genes with the mean differences less than the threshold will be #' selected for component merging. It is used in the three-component setting, #' and is enabled when if.filter = TRUE. The default is 0.25. #' @param tol The convergence criterion. The default is 10^(-5). #' @param pi01 Initialized proportion for first kown component. The default is #' \eqn{Null} and pi01 will be generated randomly from uniform distribution. #' @param pi02 Initialized proportion for second kown component. pi02 is needed #' only for running a three-component model. The default is \eqn{Null} and pi02 #' will be generated randomly from uniform distribution. #' @param nthread The number of threads used for deconvolution when OpenMP is #' available in the system. The default is the number of whole threads minus #' one. In our no-OpenMP version, it is set to 1. #' @return \item{pi}{A matrix of estimated proportion. First row and second row #' corresponds to the proportion estimate for the known components and unkown #' component respectively for two or three component settings, and each column #' corresponds to one sample.} \item{pi.iter}{Estimated proportions in each #' iteration. It is a \eqn{niter *Ny*p} array, where \eqn{p} is the number of #' components. This is enabled only when output.more.info = TRUE.} #' \item{gene.name}{The names of genes used in estimating the proportions. If #' no gene names are provided in the original data set, the genes will be #' automatically indexed.} #' @author Zeya Wang, Wenyi Wang #' @seealso http://bioinformatics.mdanderson.org/main/DeMixT #' @references Wang Z, Cao S, Morris J S, et al. Transcriptome Deconvolution of #' Heterogeneous Tumor Samples with Immune Infiltration. iScience, 2018, 9: #' 451-460. #' @keywords DeMixT_DE #' @examples #' #' # Example 1: estimate proportions for simulated two-component data #' # with spike-in normal reference #' data(test.data.2comp) #' # res.DE = DeMixT_DE(data.Y = test.data.2comp$data.Y, #' # data.N1 = test.data.2comp$data.N1, #' # niter = 10, nbin = 50, nspikein = 50, #' # if.filter = TRUE, #' # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, #' # tol = 10^(-5)) #' # #' # Example 2: estimate proportions for simulated two-component data #' # without spike-in normal reference #' # data(test.data.2comp) #' # res.DE = DeMixT_DE(data.Y = test.data.2comp$data.Y, #' # data.N1 = test.data.2comp$data.N1, #' # niter = 10, nbin = 50, nspikein = 0, #' # if.filter = TRUE, #' # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, #' # tol = 10^(-5)) #' # #' # Example 3: estimate proportions for simulated three-component #' # mixed cell line data #' # data(test.data.3comp) #' # res.DE <- DeMixT_DE(data.Y = test.data.3comp$data.Y, #' # data.N1 = test.data.3comp$data.N1, #' # data.N2 = test.data.3comp$data.N2, #' # if.filter = TRUE) #' #' DeMixT_DE <- function (data.Y, data.N1, data.N2 = NULL, niter = 10, nbin = 50, if.filter = TRUE, filter.sd = 0.5, ngene.selected.for.pi = NA, nspikein = min(200, ceiling(ncol(data.Y)*0.3)), mean.diff.in.CM = 0.25, tol = 10^(-5), pi01 = NULL, pi02 = NULL, nthread = parallel::detectCores() - 1) { filter.option = 1 ## Transfering data format data.Y <- SummarizedExperiment::assays(data.Y)[[1]] data.N1 <- SummarizedExperiment::assays(data.N1)[[1]] if (is.na(ngene.selected.for.pi)){ ngene.selected.for.pi = min(1500, round(0.3*nrow(data.Y))) } nS = ncol(data.Y) ## Generate Spike-in normal reference if (!is.null(data.N2)) nspikein = 0 if (is.null(nspikein)) nspikein = min(200, ceiling(ncol(data.Y)*0.3)) if (nspikein > 0){ MuN = apply(log2(data.N1), 1, mean) MuN[which(!is.finite(MuN))] = apply(log2(data.N1[which(!is.finite(MuN)), ] + 0.1), 1, mean) SigmaN = apply(log2(data.N1), 1, sd) SigmaN[which(!is.finite(SigmaN))] = apply(log2(data.N1[which(!is.finite(SigmaN)), ] + 0.1), 1, sd) Spikein.normal = array(0, c(nrow(data.Y), nspikein)) for(k in 1:nrow(data.Y)) { Spikein.normal[k, ] = 2^rnorm(n = nspikein, mean = MuN[k], sd = SigmaN[k]) } data.Y = cbind(data.Y, Spikein.normal) } if (!is.null(data.N2)) data.N2 <- SummarizedExperiment::assays(data.N2)[[1]] ## Creat row and column names for input data if (is.null(rownames(data.Y))) { rownames(data.Y) <- paste('Gene', seq = seq(1, nrow(data.Y))) rownames(data.N1) <- paste('Gene', seq = seq(1, nrow(data.N1))) if(!is.null(data.N2)){ rownames(data.N2) <- paste('Gene', seq = seq(1, nrow(data.N2))) } } if (is.null(colnames(data.Y))) { colnames(data.Y) <- paste('Sample', seq = seq(1, ncol(data.Y))) colnames(data.N1) <- paste('Sample', seq = seq(1, ncol(data.N1))) if(!is.null(data.N2)){ colnames(data.N2) <- paste('Sample', seq = seq(1, ncol(data.N2))) } } ## Merge the inputdata and creat group id if (is.null(data.N2)) { inputdata <- cbind(data.N1, data.Y) groupid <- c(rep(1, ncol(data.N1)), rep(3, ncol(data.Y))) }else { inputdata <- cbind(data.N1, data.N2, data.Y) groupid <- c(rep(1, ncol(data.N1)), rep(2, ncol(data.N2)), rep(3, ncol(data.Y))) } ## filter.option: 1 - remove genes containing zero; 2 - add 1 to to kill zeros if (filter.option == 1) { index <- apply(inputdata, 1, function(x) sum(x <= 0) == 0) inputdata <- inputdata[index, ] data.N1 <- data.N1[index, ] if (!is.null(data.N2)) data.N2 <- data.N2[index, ] data.Y <- data.Y[index, ] }else if (filter.option == 2) { inputdata <- inputdata + 1 data.N1 <- data.N1 + 1 if (!is.null(data.N2)) data.N2 <- data.N2 + 1 data.Y <- data.Y + 1 }else { stop("The argument filter.option can only be 1 or 2") } ## filter out genes with constant value across all samples if (is.null(data.N2)) { if (dim(inputdata)[1] == 1) { inputdata <- t(as.matrix(inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1), ])) } else { inputdata <- inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1), ] } }else{ if (dim(inputdata)[1] == 1) { inputdata <- t(as.matrix(inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1) & apply(data.N2, 1, function(x) length(unique(x)) > 1), ])) } else { inputdata <- inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1) & apply(data.N2, 1, function(x) length(unique(x)) > 1), ] } } filter2 <- function(inputdata1r, ngene.selected.for.pi, n = 1) { if ((ngene.selected.for.pi > 1) & (ngene.selected.for.pi%%1 == 0)) { id2 <- order(inputdata1r, decreasing = TRUE) id2 <- id2[seq(1, min(n * ngene.selected.for.pi, length(inputdata1r)))] } else if ((ngene.selected.for.pi < 1) & (ngene.selected.for.pi > 0)) { id2 <- (inputdata1r > quantile(inputdata1r, probs = 1 - n * ngene.selected.for.pi)) } else { stop("The argument ngene.selected.for.pi can only be an integer or a percentage between 0 and 1") } if (sum(id2) < 20) stop("there are too few genes for filtering stage 1.\n Please relax threshold for filtering ") return(inputdatamat1[id2, ]) } inputdata = as.matrix(inputdata) if (if.filter == FALSE) { gene.name <- rownames(inputdata) res <- Optimum_KernelC(inputdata, groupid, setting.pi = 0, nspikein = nspikein, givenpi = rep(0, 2 * ncol(data.Y)), givenpiT = rep(0, ncol(data.Y)), niter = niter, ninteg = nbin, tol = tol, pi01 = pi01, pi02 = pi02, nthread = nthread) } if (if.filter == TRUE & is.null(data.N2)) { #inputdatans <- rowSds(log2(data.N1)) inputdatans <- apply(log2(data.N1), 1, sd) id1 <- (inputdatans < filter.sd) if (sum(id1) < 20) stop("The threshold of standard variation is too stringent. \n Please provide a larger threshold. ") inputdatamat1 <- inputdata[id1, ] inputdatamat1nm <- rowMeans(inputdatamat1[, groupid == 1]) inputdatamat1ym <- rowMeans(inputdatamat1[, groupid == 3]) inputdata1r <- inputdatamat1ym/inputdatamat1nm inputdata2 <- filter2(inputdata1r, ngene.selected.for.pi) gene.name <- rownames(inputdata2) res <- Optimum_KernelC(inputdata2, groupid, setting.pi = 0, nspikein = nspikein, givenpi = rep(0, 2 * ncol(data.Y)), givenpiT = rep(0, ncol(data.Y)), niter = niter, ninteg = nbin, tol = tol, pi01 = pi01, pi02 = pi02, nthread = nthread) } if (if.filter == TRUE & !is.null(data.N2)) { message("Fitering stage 1 starts\n") inputdatan1m <- rowMeans(log2(inputdata[, groupid == 1])) inputdatan2m <- rowMeans(log2(inputdata[, groupid == 2])) #inputdatan1s <- rowSds(log2(inputdata[, groupid == 1])) inputdatan1s <- apply(log2(inputdata[, groupid == 1]), 1, sd) #inputdatan2s <- rowSds(log2(inputdata[, groupid == 2])) inputdatan2s <- apply(log2(inputdata[, groupid == 2]), 1, sd) id1 <- ((abs(inputdatan1m - inputdatan2m) < mean.diff.in.CM) & (inputdatan1s < filter.sd) & (inputdatan2s < filter.sd)) if (sum(id1) < 20) stop("The thresholds of standard variation and \n mean difference are too stringent. \n Please provide larger thresholds") inputdatamat1 <- inputdata[id1, ] inputdatamat1nm <- rowMeans(inputdatamat1[, (groupid == 1) | (groupid == 2)]) inputdatamat1ym <- rowMeans(inputdatamat1[, (groupid == 3)]) #inputdata1r <- rowSds(inputdatamat1[, groupid == 3]) inputdata1r <- apply(inputdatamat1[, groupid == 3], 1, sd) inputdatamat2 <- filter2(inputdata1r, ngene.selected.for.pi) cnvgroup <- groupid cnvgroup[groupid == 2] <- 1 res1 <- Optimum_KernelC(inputdatamat2, cnvgroup, setting.pi = 0, nspikein = nspikein, givenpi = rep(0, ncol(data.Y)), givenpiT = rep(0, ncol(data.Y)), niter = niter, ninteg = nbin, tol = tol, pi01 = pi01, pi02 = pi02, nthread = nthread) fixed.piT <- 1 - as.numeric(res1$pi[1, ]) message("Filtering stage 1 is finished\n") message("Filtering stage 2 starts\n") id3 <- ((inputdatan1s < filter.sd) & (inputdatan2s < filter.sd)) if (sum(id3) < 20) stop("The thresholds of standard variation and \n mean difference are too stringent. \n Please provide larger thresholds") inputdatamat1 <- inputdata[id3, ] inputdatan1m <- rowMeans(log2(inputdatamat1[, groupid == 1])) inputdatan2m <- rowMeans(log2(inputdatamat1[, groupid == 2])) inputdata1d <- abs(inputdatan1m - inputdatan2m) inputdatamat2 <- filter2(inputdata1d, ngene.selected.for.pi, 2) #inputdata1s <- rowSds(inputdatamat2[, groupid == 3]) inputdata1s <- apply(inputdatamat2[, groupid == 3], 1, sd) id5 <- (inputdata1s > quantile(inputdata1s, probs = 0.5)) inputdatamat3 <- inputdatamat2[id5, ] gene.name <- rownames(inputdatamat3) res <- Optimum_KernelC(inputdatamat3, groupid, nspikein = nspikein, setting.pi = 2, givenpi = rep(0, 2 * ncol(data.Y)), givenpiT = fixed.piT, niter = niter, ninteg = nbin, tol = tol, pi01 = pi01, pi02 = pi02, nthread = nthread) message("Filtering stage 2 is finished") } if(nspikein == 0){ pi <- rbind(t(as.matrix(res$pi[1, ])), 1 - t(as.matrix(res$pi[1, ]))) row.names(pi) <- c("PiN1", "PiT") colnames(pi) <- colnames(data.Y) pi1 <- as.matrix(res$pi1) colnames(pi1) <- as.character(seq(1, ncol(pi1))) row.names(pi1) = colnames(data.Y) ## Save pi.iter pi.iter <- array(0, c(ncol(res$pi1), nrow(res$pi1), 2)) pi.iter[ , , 1] <- t(res$pi1) pi.iter[ , , 2] <- 1 - pi.iter[ , , 1] colnames(pi.iter) <- colnames(data.Y) row.names(pi.iter) <- paste('iter ', seq = c(1:nrow(pi.iter))) }else{ pi <- rbind(t(as.matrix(res$pi[1, c(1:nS)])), 1 - t(as.matrix(res$pi[1, c(1:nS)]))) row.names(pi) <- c("PiN1", "PiT") colnames(pi) <- colnames(data.Y)[c(1:nS)] pi1 <- as.matrix(res$pi1[c(1:nS),]) colnames(pi1) <- as.character(seq(1, ncol(pi1))) row.names(pi1) = colnames(data.Y[,c(1:nS)]) ## Save pi.iter pi.iter <- array(0, c(ncol(res$pi1), nrow(res$pi1)-nspikein, 2)) pi.iter[ , , 1] <- t(res$pi1)[,c(1:nS)] pi.iter[ , , 2] <- 1 - pi.iter[ , , 1] colnames(pi.iter) <- colnames(data.Y)[1:nS] row.names(pi.iter) <- paste('iter ', seq = c(1:nrow(pi.iter))) } if(IQR(pi[2,]) < 0.01){ stop('DeMixT Estimation Failed, try different initial values for pi') } if (!is.null(data.N2)) { pi <- rbind(res$pi, t(as.matrix(apply(res$pi, 2, function(x) 1-sum(x))))) row.names(pi) <- c("PiN1" ,"PiN2", "PiT") colnames(pi) <- colnames(data.Y) pi2 <- as.matrix(res$pi2) colnames(pi2) <- as.character(seq(1, ncol(pi2))) row.names(pi2) = colnames(data.Y) ## Save pi.iter pi.iter <- array(0, c(ncol(res$pi1), nrow(res$pi1), 3)) pi.iter[ , , 1] <- t(res$pi1) pi.iter[ , , 2] <- t(res$pi2) pi.iter[ , , 3] <- 1 - (pi.iter[ , , 1] + pi.iter[ , , 2]) # pi.iter <- array(t(rbind(res$pi1, res$pi2)), # dim <- c(ncol(res$pi1), nrow(res$pi1), 2)) colnames(pi.iter) <- colnames(data.Y) row.names(pi.iter) <- paste('iter ', seq = c(1:nrow(pi.iter))) } cat("Estimation of Proportions finished:\n") print(round(t(pi),4)) return(list(pi = pi, pi.iter = pi.iter, gene.name = gene.name)) } DeMixT/R/DeMixT_GS.R0000644000175200017520000005661214710233064015007 0ustar00biocbuildbiocbuild#' Estimates the proportions of mixed samples for each mixing component using #' profile likelihood gene selection #' #' This function is designed to estimate the proportions of all mixed samples #' for each mixing component with a new proposed profile likelihood based gene #' selection, which can select most identifiable genes as reference gene sets #' to achieve better model fitting quality. We first calculated the Hessian #' matrix of the parameter spaces and then derive the confidence interval of #' the profile likelihood of each gene. We then utilized the length of #' confidence interval as a metric to rank the identifiability of genes. As a #' result, the proposed gene selection approach can improve the tumor-specific #' transcripts proportion estimation. #' #' #' @param data.Y A SummarizedExperiment object of expression data from mixed #' tumor samples. It is a \eqn{G} by \eqn{My} matrix where \eqn{G} is the #' number of genes and \eqn{My} is the number of mixed samples. Samples with #' the same tissue type should be placed together in columns. #' @param data.N1 A SummarizedExperiment object of expression data from #' reference component 1 (e.g., normal). It is a \eqn{G} by \eqn{M1} matrix #' where \eqn{G} is the number of genes and \eqn{M1} is the number of samples #' for component 1. #' @param data.N2 A SummarizedExperiment object of expression data from #' additional reference samples. It is a \eqn{G} by \eqn{M2} matrix where #' \eqn{G} is the number of genes and \eqn{M2} is the number of samples for #' component 2. Component 2 is needed only for running a three-component model. #' @param niter The maximum number of iterations used in the algorithm of #' iterated conditional modes. A larger value better guarantees the convergence #' in estimation but increases the running time. The default is 10. #' @param nbin The number of bins used in numerical integration for computing #' complete likelihood. A larger value increases accuracy in estimation but #' increases the running time, especially in a three-component deconvolution #' problem. The default is 50. #' @param if.filter The logical flag indicating whether a predetermined filter #' rule is used to select genes for proportion estimation. The default is TRUE. #' @param filter.sd The cut-off for the standard deviation of lognormal #' distribution. Genes whose log transferred standard deviation smaller than #' the cut-off will be selected into the model. The default is TRUE. #' @param ngene.Profile.selected The number of genes used for proportion #' estimation ranked by profile likelihood. The default is #' \eqn{min(1500,0.1*G)}, where \eqn{G} is the number of genes. #' @param ngene.selected.for.pi The percentage or the number of genes used for #' proportion estimation. The difference between the expression levels from #' mixed tumor samples and the known component(s) are evaluated, and the most #' differential expressed genes are selected, which is called DE. It is enabled #' when if.filter = TRUE. The default is \eqn{min(1500, 0.3*G)}, where \eqn{G} #' is the number of genes. Users can also try using more genes, ranging from #' \eqn{0.3*G} to \eqn{0.5*G}, and evaluate the outcome. #' @param mean.diff.in.CM Threshold of expression difference for selecting #' genes in the component merging strategy. We merge three-component to #' two-component by selecting genes with similar expressions for the two known #' components. Genes with the mean differences less than the threshold will be #' selected for component merging. It is used in the three-component setting, #' and is enabled when if.filter = TRUE. The default is 0.25. #' @param nspikein The number of spikes in normal reference used for proportion #' estimation. The default value is \eqn{ min(200, 0.3*My)}, where \eqn{My} the #' number of mixed samples. If it is set to 0, proportion estimation is #' performed without any spike in normal reference. #' @param tol The convergence criterion. The default is 10^(-5). #' @param pi01 Initialized proportion for first kown component. The default is #' \eqn{Null} and pi01 will be generated randomly from uniform distribution. #' @param pi02 Initialized proportion for second kown component. pi02 is needed #' only for running a three-component model. The default is \eqn{Null} and pi02 #' will be generated randomly from uniform distribution. #' @param nthread The number of threads used for deconvolution when OpenMP is #' available in the system. The default is the number of whole threads minus #' one. In our no-OpenMP version, it is set to 1. #' @return \item{pi}{A matrix of estimated proportion. First row and second row #' corresponds to the proportion estimate for the known components and unkown #' component respectively for two or three component settings, and each column #' corresponds to one sample.} \item{pi.iter}{Estimated proportions in each #' iteration. It is a \eqn{niter *My*p} array, where \eqn{p} is the number of #' components. This is enabled only when output.more.info = TRUE.} #' \item{gene.name}{The names of genes used in estimating the proportions. If #' no gene names are provided in the original data set, the genes will be #' automatically indexed.} #' @note A Hessian matrix file will be created in the working directory and the #' corresponding Hessian matrix with an encoded name from the mixed tumor #' sample data will be saved under this file. If a user reruns this function #' with the same dataset, this Hessian matrix will be loaded to in place of #' running the profile likelihood method and reduce running time. #' @author Shaolong Cao, Zeya Wang, Wenyi Wang #' @seealso http://bioinformatics.mdanderson.org/main/DeMixT #' @references Gene Selection and Identifiability Analysis of RNA Deconvolution #' Models using Profile Likelihood. Manuscript in preparation. #' @keywords DeMixT_GS #' @examples #' #' #' # Example 1: estimate proportions for simulated two-component data #' # with spike-in normal reference #' data(test.data.2comp) #' # res.GS = DeMixT_GS(data.Y = test.data.2comp$data.Y, #' # data.N1 = test.data.2comp$data.N1, #' # niter = 10, nbin = 50, nspikein = 50, #' # if.filter = TRUE, ngene.Profile.selected = 150, #' # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, #' # tol = 10^(-5)) #' # #' # Example 2: estimate proportions for simulated two-component data #' # without spike-in normal reference #' # data(test.dtat.2comp) #' # res.GS = DeMixT_GS(data.Y = test.data.2comp$data.Y, #' # data.N1 = test.data.2comp$data.N1, #' # niter = 10, nbin = 50, nspikein = 0, #' # if.filter = TRUE, ngene.Profile.selected = 150, #' # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, #' # tol = 10^(-5)) #' #' #' #' @export DeMixT_GS DeMixT_GS <- function(data.Y, data.N1, data.N2 = NULL, niter = 10, nbin = 50, if.filter = TRUE, filter.sd = 0.5, ngene.Profile.selected = NA, ngene.selected.for.pi = NA, mean.diff.in.CM = 0.25, nspikein = min(200, ceiling(ncol(data.Y)*0.3)), tol = 10^(-5), pi01 = NULL, pi02 = NULL, nthread = parallel::detectCores() - 1) { IF_inverse <- function(m){ return(class(try(solve(m), silent=T))[1] == "matrix") } ## Creat a folder for saving hessian matrix path = getwd() if (!file.exists(paste0(path,'/Hessian_Matrix'))){ dir.create(path = paste0(path, '/Hessian_Matrix')) } ## Transfering data format data.Y <- SummarizedExperiment::assays(data.Y)[[1]] data.N1 <- SummarizedExperiment::assays(data.N1)[[1]] nS = ncol(data.Y) ## Encode data.Y for hessian matrix if(nS > 10){ data.Y_encode = base64encode(log2(as.matrix(data.Y)[1, 1:10] + 1)) }else{ data.Y_encode = base64encode(log2(as.matrix(data.Y)[1, 1:nS] + 1)) } if (!is.null(data.N2)){ message('File for Hessian matrix has existed') data.N2 <- SummarizedExperiment::assays(data.N2)[[1]] nspikein = 0 } if (is.na(ngene.selected.for.pi)){ ngene.selected.for.pi = min(1500, round(0.3*nrow(data.Y))) } ## Generate Spike-in normal reference if (is.null(nspikein)) nspikein = min(200, ceiling(ncol(data.Y)*0.3)) if (nspikein > 0){ MuN = apply(log2(data.N1), 1, mean) MuN[which(!is.finite(MuN))] = apply(log2(data.N1[which(!is.finite(MuN)), ] + 0.1), 1, mean) SigmaN = apply(log2(data.N1), 1, sd) SigmaN[which(!is.finite(SigmaN))] = apply(log2(data.N1[which(!is.finite(SigmaN)), ] + 0.1), 1, sd) Spikein.normal = array(0, c(nrow(data.Y), nspikein)) for(k in 1:nrow(data.Y)) { Spikein.normal[k, ] = 2^rnorm(n = nspikein, mean = MuN[k], sd = SigmaN[k]) } data.Y = cbind(data.Y, Spikein.normal) } ## Creat row and column names for input data if (is.null(rownames(data.Y))) { rownames(data.Y) <- paste('Gene', seq = seq(1, nrow(data.Y))) rownames(data.N1) <- paste('Gene', seq = seq(1, nrow(data.N1))) if(!is.null(data.N2)){ rownames(data.N2) <- paste('Gene', seq = seq(1, nrow(data.N2))) } } if (is.null(colnames(data.Y))) { colnames(data.Y) <- paste('Sample', seq = seq(1, ncol(data.Y))) colnames(data.N1) <- paste('Sample', seq = seq(1, ncol(data.N1))) if(!is.null(data.N2)){ colnames(data.N2) <- paste('Sample', seq = seq(1, ncol(data.N2))) } } ## Merge the inputdata and creat group id if (is.null(data.N2)) { inputdata <- cbind(data.N1, data.Y) groupid <- c(rep(1, ncol(data.N1)), rep(3, ncol(data.Y))) }else { inputdata <- cbind(data.N1, data.N2, data.Y) groupid <- c(rep(1, ncol(data.N1)), rep(2, ncol(data.N2)), rep(3, ncol(data.Y))) } ## Create the default value for ngene.Profile.selected filter.option = 1 if(is.na(ngene.Profile.selected)){ ngene.Profile.selected<-min(1500, round(0.3*nrow(data.Y))) } ## filter.option: 1 - remove genes containing zero; 2 - add 1 to to kill zeros if (filter.option == 1) { index <- apply(inputdata, 1, function(x) sum(x <= 0) == 0) inputdata <- inputdata[index, ] data.N1 <- data.N1[index, ] if (!is.null(data.N2)) data.N2 <- data.N2[index, ] data.Y <- data.Y[index, ] }else if (filter.option == 2) { inputdata <- inputdata + 1 data.N1 <- data.N1 + 1 if (!is.null(data.N2)) data.N2 <- data.N2 + 1 data.Y <- data.Y + 1 }else { stop("The argument filter.option can only be 1 or 2") } ## filter out genes with constant value across all samples if (is.null(data.N2)) { if (dim(inputdata)[1] == 1) { inputdata <- t(as.matrix(inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1), ])) } else { inputdata <- inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1), ] } }else{ if (dim(inputdata)[1] == 1) { inputdata <- t(as.matrix(inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1) & apply(data.N2, 1, function(x) length(unique(x)) > 1), ])) } else { inputdata <- inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1) & apply(data.N2, 1, function(x) length(unique(x)) > 1), ] } } filter2 <- function(inputdata1r, ngene.selected.for.pi, n = 1) { if ((ngene.selected.for.pi > 1) & (ngene.selected.for.pi%%1 == 0)) { id2 <- order(inputdata1r, decreasing = TRUE) id2 <- id2[seq(1, min(n * ngene.selected.for.pi, length(inputdata1r)))] } else if ((ngene.selected.for.pi < 1) & (ngene.selected.for.pi > 0)) { id2 <- (inputdata1r > quantile(inputdata1r, probs = 1 - n * ngene.selected.for.pi)) } else { stop("The argument ngene.selected.for.pi can only be an integer or a percentage between 0 and 1") } if (sum(id2) < 20) stop("there are too few genes for filtering stage 1.\n Please relax threshold for filtering ") return(inputdatamat1[id2, ]) } inputdata = as.matrix(inputdata) ## case 1 if (if.filter == FALSE) { gene.name <- rownames(inputdata) res <- Optimum_KernelC(inputdata, groupid, setting.pi = 0, nspikein = nspikein, givenpi = rep(0, 2 * ncol(data.Y)), givenpiT = rep(0, ncol(data.Y)), niter = niter, ninteg = nbin, tol = tol, pi01 = pi01, pi02 = pi02, nthread = nthread) } ## Gene selection based on profile likelihood method if (if.filter == TRUE & is.null(data.N2)) { # step 2 identifiability selection cat("Gene selection starts\n") inputdatans <- apply(log2(data.N1), 1, sd) id1 <- (inputdatans < filter.sd) if (sum(id1) < 20) stop("The threshold of standard variation is too stringent. \n Please provide a larger threshold. ") inputdatamat1 <- inputdata[id1, ] inputdatamat1nm <- rowMeans(inputdatamat1[, groupid == 1]) inputdatamat1ym <- rowMeans(inputdatamat1[, groupid == 3]) inputdata1r <- inputdatamat1ym/inputdatamat1nm inputdata2 <- filter2(inputdata1r, ngene.selected.for.pi) gene.name.S1 <- rownames(inputdata2) res.S1 <- Optimum_KernelC(inputdata2, groupid, setting.pi = 0, nspikein = nspikein, givenpi = rep(0, 2 * ncol(data.Y)), givenpiT = rep(0, ncol(data.Y)), niter = niter, ninteg = nbin, tol = tol, pi01 = pi01, pi02 = pi02, nthread = nthread) Pi.all<-colSums(res.S1$pi) PiT.all <- 1-colSums(res.S1$pi) MuT.S1 <- as.numeric(res.S1$decovMu[,dim(res.S1$decovMu)[2]]) SigmaT.S1 <- as.numeric(res.S1$decovSigma[,dim(res.S1$decovMu)[2]]) sdn.obs<-apply(log2(inputdata2[, groupid == 1]+0.001),1,sd) mun.obs<-rowMeans(log2(inputdata2[, groupid == 1]+0.001)) n.normal<-sum(groupid == 1) n.mix<-sum(groupid == 3) res.all <- Optimum_KernelC(inputdatamat1, groupid, setting.pi = 1, nspikein = nspikein, givenpi = Pi.all, givenpiT = PiT.all, niter = 1, ninteg = nbin, pi01 = pi01, pi02 = pi02, tol = tol, nthread = nthread) MuT.all <- as.numeric(res.all$decovMu[,dim(res.all$decovMu)[2]]) SigmaT.all <- as.numeric(res.all$decovSigma[,dim(res.all$decovMu)[2]]) sdn.obs<-apply(log2(inputdatamat1[, groupid == 1]+0.001),1,sd) mun.obs<-rowMeans(log2(inputdatamat1[, groupid == 1]+0.001)) n.normal<-sum(groupid == 1) n.mix<-sum(groupid == 3) if(file.exists(paste0(path, '/Hessian_Matrix/', data.Y_encode,'_Hessian.RData'))){ load(paste0(path, '/Hessian_Matrix/', data.Y_encode,'_Hessian.RData')) message(paste0('Loading Hessian matrix from ', paste0(path, '/Hessian_Matrix/', data.Y_encode,'_Hessian.RData \n'))) }else{ if(nspikein > 0){ Hessian <- D2Loglikelihood_2D(y=inputdatamat1[,(n.normal+1): (n.normal+n.mix-nspikein)], Pi=Pi.all, MuN=mun.obs, MuT=MuT.all, SigmaN=sdn.obs, SigmaT=SigmaT.all) }else{ Hessian <- D2Loglikelihood_2D(y=inputdatamat1[,(n.normal+1): (n.normal+n.mix)], Pi=Pi.all, MuN=mun.obs, MuT=MuT.all, SigmaN=sdn.obs, SigmaT=SigmaT.all) } save(Hessian, file = paste0(path, '/Hessian_Matrix/', data.Y_encode,'_Hessian.RData')) message(paste0('Hessian matrix has been saved in ', paste0(path, '/Hessian_Matrix/', data.Y_encode,'_Hessian.RData \n'))) } ## Check if the Hessian matrix contains infinity if(sum(!is.finite(Hessian)) == 0){ if(nspikein > 0 ){ S <- n.mix - nspikein }else{ S <- n.mix } G <- nrow(inputdatamat1) if(IF_inverse(Hessian)){ C.inverse <- 2*diag(solve(Hessian)) }else{ C.inverse <- rep(NA, S) } C.marginal <- abs(2/diag(Hessian)) if(sum(is.na(C.inverse))>1){ C <- C.marginal Non.inverse<-TRUE }else{ C <- C.inverse Non.inverse<-FALSE } if(nspikein > 0){ theta.valid <- x_update_2D(Pi.all[c(1:(length(Pi.all) - nspikein))], MuT.all, SigmaT.all, S, G) }else{ theta.valid <- x_update_2D(Pi.all, MuT.all, SigmaT.all, S, G) } CI.upper <- theta.valid + sqrt(abs(qchisq(0.95,1)*C)) CI.upper.MuT <- x_update_inv_2D(CI.upper,S,G)$MuT CI.upper.SigmaT <- x_update_inv_2D(CI.upper,S,G)$SigmaT CI.lower <- theta.valid - sqrt(abs(qchisq(0.95,1)*C)) CI.lower.MuT <- x_update_inv_2D(CI.lower,S,G)$MuT CI.lower.SigmaT <- x_update_inv_2D(CI.lower,S,G)$SigmaT id1.ID <- order(CI.upper.MuT-CI.lower.MuT) id2 <- id1.ID[1:min(ngene.Profile.selected, length(id1.ID))] inputmat<-inputdatamat1[id2,] pi01 = Pi.all; pi02 = PiT.all; res <- Optimum_KernelC(inputmat, groupid, setting.pi = 0, nspikein = nspikein, givenpi = rep(0, 2 * n.mix), givenpiT = rep(0, n.mix), niter = niter, ninteg = nbin, tol = tol, pi01 = pi01, pi02 = pi02, nthread=nthread) gene.name <- rownames(inputmat) }else{ message('Hessian matrix has infinity value, switch to S1 \n') res = res.S1 gene.name <- rownames(inputdata2) } } ## case 3: two-stage filtering, three components if (if.filter == TRUE & !is.null(data.N2)) { message("Fitering stage 1 starts\n") inputdatan1m <- rowMeans(log2(inputdata[, groupid == 1])) inputdatan2m <- rowMeans(log2(inputdata[, groupid == 2])) #inputdatan1s <- rowSds(log2(inputdata[, groupid == 1])) inputdatan1s <- apply(log2(inputdata[, groupid == 1]), 1, sd) #inputdatan2s <- rowSds(log2(inputdata[, groupid == 2])) inputdatan2s <- apply(log2(inputdata[, groupid == 2]), 1, sd) id1 <- ((abs(inputdatan1m - inputdatan2m) < mean.diff.in.CM) & (inputdatan1s < filter.sd) & (inputdatan2s < filter.sd)) if (sum(id1) < 20) stop("The thresholds of standard variation and \n mean difference are too stringent. \n Please provide larger thresholds") inputdatamat1 <- inputdata[id1, ] inputdatamat1nm <- rowMeans(inputdatamat1[, (groupid == 1) | (groupid == 2)]) inputdatamat1ym <- rowMeans(inputdatamat1[, (groupid == 3)]) #inputdata1r <- rowSds(inputdatamat1[, groupid == 3]) inputdata1r <- apply(inputdatamat1[, groupid == 3], 1, sd) inputdatamat2 <- filter2(inputdata1r, ngene.selected.for.pi) cnvgroup <- groupid cnvgroup[groupid == 2] <- 1 res1 <- Optimum_KernelC(inputdatamat2, cnvgroup, setting.pi = 0, nspikein = nspikein, givenpi = rep(0, ncol(data.Y)), givenpiT = rep(0, ncol(data.Y)), niter = niter, ninteg = nbin, tol = tol, pi01 = pi01, pi02 = pi02, nthread = nthread) fixed.piT <- 1 - as.numeric(res1$pi[1, ]) message("Filtering stage 1 is finished\n") message("Filtering stage 2 starts\n") id3 <- ((inputdatan1s < filter.sd) & (inputdatan2s < filter.sd)) if (sum(id3) < 20) stop("The thresholds of standard variation and \n mean difference are too stringent. \n Please provide larger thresholds") inputdatamat1 <- inputdata[id3, ] inputdatan1m <- rowMeans(log2(inputdatamat1[, groupid == 1])) inputdatan2m <- rowMeans(log2(inputdatamat1[, groupid == 2])) inputdata1d <- abs(inputdatan1m - inputdatan2m) inputdatamat2 <- filter2(inputdata1d, ngene.selected.for.pi, 2) #inputdata1s <- rowSds(inputdatamat2[, groupid == 3]) inputdata1s <- apply(inputdatamat2[, groupid == 3], 1, sd) id5 <- (inputdata1s > quantile(inputdata1s, probs = 0.5)) inputdatamat3 <- inputdatamat2[id5, ] gene.name <- rownames(inputdatamat3) res <- Optimum_KernelC(inputdatamat3, groupid, setting.pi = 2, nspikein = nspikein, givenpi = rep(0, 2 * ncol(data.Y)), givenpiT = fixed.piT, niter = niter, ninteg = nbin, tol = tol, pi01 = pi01, pi02 = pi02, nthread = nthread) message("Filtering stage 2 is finished") } if(nspikein == 0){ pi <- rbind(t(as.matrix(res$pi[1, ])), 1 - t(as.matrix(res$pi[1, ]))) row.names(pi) <- c("PiN1", "PiT") colnames(pi) <- colnames(data.Y) pi1 <- as.matrix(res$pi1) colnames(pi1) <- as.character(seq(1, ncol(pi1))) row.names(pi1) = colnames(data.Y) ## Save pi.iter pi.iter <- array(0, c(ncol(res$pi1), nrow(res$pi1), 2)) pi.iter[ , , 1] <- t(res$pi1) pi.iter[ , , 2] <- 1 - pi.iter[ , , 1] colnames(pi.iter) <- colnames(data.Y) row.names(pi.iter) <- paste('iter ', seq = c(1:nrow(pi.iter))) }else{ pi <- rbind(t(as.matrix(res$pi[1, c(1:nS)])), 1 - t(as.matrix(res$pi[1, c(1:nS)]))) row.names(pi) <- c("PiN1", "PiT") colnames(pi) <- colnames(data.Y)[c(1:nS)] pi1 <- as.matrix(res$pi1[c(1:nS),]) colnames(pi1) <- as.character(seq(1, ncol(pi1))) row.names(pi1) = colnames(data.Y[,c(1:nS)]) ## Save pi.iter pi.iter <- array(0, c(ncol(res$pi1), nrow(res$pi1)-nspikein, 2)) pi.iter[ , , 1] <- t(res$pi1)[,c(1:nS)] pi.iter[ , , 2] <- 1 - pi.iter[ , , 1] colnames(pi.iter) <- colnames(data.Y)[1:nS] row.names(pi.iter) <- paste('iter ', seq = c(1:nrow(pi.iter))) } if(IQR(pi[2,]) < 0.01){ stop('DeMixT Estimation Failed, try different initial values for pi') } if (!is.null(data.N2)) { pi <- rbind(res$pi, t(as.matrix(apply(res$pi, 2, function(x) 1-sum(x))))) row.names(pi) <- c("PiN1" ,"PiN2", "PiT") colnames(pi) <- colnames(data.Y) pi2 <- as.matrix(res$pi2) colnames(pi2) <- as.character(seq(1, ncol(pi2))) row.names(pi2) = colnames(data.Y) ## Save pi.iter pi.iter <- array(0, c(ncol(res$pi1), nrow(res$pi1), 3)) pi.iter[ , , 1] <- t(res$pi1) pi.iter[ , , 2] <- t(res$pi2) pi.iter[ , , 3] <- 1 - (pi.iter[ , , 1] + pi.iter[ , , 2]) # pi.iter <- array(t(rbind(res$pi1, res$pi2)), # dim <- c(ncol(res$pi1), nrow(res$pi1), 2)) colnames(pi.iter) <- colnames(data.Y) row.names(pi.iter) <- paste('iter ', seq = c(1:nrow(pi.iter))) } cat("Estimation of Proportions finished:\n") print(round(t(pi),4)) return(list(pi = pi, pi.iter = pi.iter, gene.name = gene.name)) } DeMixT/R/DeMixT_Preprocessing.R0000644000175200017520000004376114710233064017322 0ustar00biocbuildbiocbuild#' @title detect_suspicious_sample_by_hierarchical_clustering_2comp #' @description Detect suspicious samples by a hierarchical clustering #' #' This function is designed to evaluate the separation of tumor samples and normal samples in a PCA space. #' If some normal samples appear in the tumor-sample dominated cluster, these normal samples are likely to #' be tumor samples and they are supposed to be filtered out before downstream analysis. But for those tumor #' samples appearing in the normal-sample dominated cluster, we do not remove them since they may be the ones #' with low tumor purity. #' @name detect_suspicious_sample_by_hierarchical_clustering_2comp #' @param count.matrix A matrix of raw expression count with \eqn{G} by \eqn{(My + M1)}, where \eqn{G} is the number #' of genes, \eqn{My} is the number of mixed samples and \eqn{M1} is the number of normal samples. Row names are genes #' column names are sample ids. #' @param normal.id A vector of normal sample ids #' @param tumor.id A vector of tumor sample ids #' @return list object #' #' @export detect_suspicious_sample_by_hierarchical_clustering_2comp detect_suspicious_sample_by_hierarchical_clustering_2comp <- function(count.matrix, normal.id, tumor.id){ if(length(normal.id) + length(tumor.id) != ncol(count.matrix)){ stop("Total number of normal and tumor samples in normal.id and tumor.id must be the same with the numbe of columns in count.matrix.") } normal.exp <- count.matrix[, match(normal.id, colnames(count.matrix))] tumor.exp <- count.matrix[, match(tumor.id, colnames(count.matrix))] gene.normal.mean <- apply(normal.exp, 1, function(x) mean(x)) gene.tumor.mean <- apply(tumor.exp, 1, function(x) mean(x)) gene.normal.index <- gene.normal.mean > quantile(gene.normal.mean, 0.25) gene.tumor.index <- gene.tumor.mean > quantile(gene.tumor.mean, 0.25) sTable = count.matrix[gene.normal.index | gene.tumor.index, ] rank.test.pvalue = NULL labels = c(rep("o", length(normal.id)), rep(".", length(tumor.id))) pch = c(rep(1, length(normal.id)), rep(20, length(tumor.id))) y <- rep("Normal", dim(count.matrix)[2]) y[colnames(count.matrix) %in% tumor.id] <- "Tumor" names(y) <- colnames(count.matrix) test.pvalue <- apply(sTable, 1, function(x) wilcox.test(x ~ y)$p.value) sorted.pvalue = sort.int(test.pvalue, index.return = TRUE) #select the top 1000 genes with smallest p-value top.gene.index = sorted.pvalue$ix[1:1000] #pca analysis principal.res = prcomp(t(sTable[top.gene.index, ]), retx = T, center = T, scale = T) top2.pcs <- principal.res$x[, 1:2] top2.pcs.dis <- dist(top2.pcs, method = "euclidean") top2.pcs.hclust <- hclust(top2.pcs.dis, method = "ward.D2") top2.pcs.two.clusters <- cutree(top2.pcs.hclust, k = 2) labels <- y[top2.pcs.hclust$order] hc_labels <- list() hc_labels[["label"]] <- labels hc_labels[["cluster"]] <- top2.pcs.two.clusters labels <- y[top2.pcs.hclust$order] labels[labels == "Tumor"] <- "+" labels[labels == "Normal"] <- "." labels_colors <- labels labels_colors[labels_colors == '+'] <- "blue" labels_colors[labels_colors == '.'] <- "red" par(mar = c(1, 2.5, 2, 0)) as.dendrogram(top2.pcs.hclust) %>% set("labels", labels) %>% set("labels_col", labels_colors) %>% plot(main = "Hierarchical clustering of tumor and normal samples") as.dendrogram(top2.pcs.hclust) %>% rect.dendrogram(k=2, border = 8, lty = 2, lwd = 1) legend( "topright", legend = c("Normal", "Tumor"), pch = c(20, 3), col = c("red", "blue"), bty = "n" ) return(hc_labels) } #' @title plot_sd #' @description Plot the standard deviation of log2 raw expression #' @name plot_sd #' @rdname detect_suspicious_sample_by_hierarchical_clustering_2comp #' @param count.matrix A matrix of raw expression count with \eqn{G} by \eqn{(My + M1)}, where \eqn{G} is the number #' of genes, \eqn{My} is the number of mixed samples and \eqn{M1} is the number of normal samples. Row names are genes #' column names are sample ids. #' @param normal.id A vector of normal sample ids #' @param tumor.id A vector of tumor sample ids #' @return #' #' @export plot_sd plot_sd <- function(count.matrix, normal.id, tumor.id){ if(length(normal.id) + length(tumor.id) != ncol(count.matrix)){ stop("Total number of normal and tumor samples in normal.id and tumor.id must be the same with the numbe of columns in count.matrix.") } count.matrix[which(count.matrix == 0, arr.ind = T)] = 1 sdn.obs <- apply(log2(count.matrix[, match(normal.id, colnames(count.matrix))]), 1, sd) sdm.obs <- apply(log2(count.matrix[, match(tumor.id, colnames(count.matrix))]), 1, sd) par(mfrow = c(1, 2)) plot(sdn.obs, xlab = 'Genes', ylab = 'Standard Deviation', main = 'Normal Reference') plot(sdm.obs, xlab = 'Genes', ylab = 'Standard Deviation', main = 'Mixed Tumor') par(mfrow = c(1,1)) } #' @title subset_sd #' @description Subset a count matrix given the the ranges of the standard deviations of the #' log2 expressions from the tumor and normal samples #' @name subset_sd #' @param count.matrix A matrix of raw expression count with \eqn{G} by \eqn{(My + M1)}, where \eqn{G} is the number #' of genes, \eqn{My} is the number of mixed samples and \eqn{M1} is the number of normal samples. Row names are genes #' column names are sample ids. #' @param normal.id A vector of normal sample ids #' @param tumor.id A vector of tumor sample ids #' @param cutoff_normal A vector of two numeric values, indicating the lower and upper bounds of standard deviation of #' log2 count matrix from the normal samples to subset. Default is c(0.1, 0.6) #' @param cutoff_tumor A vector of two numeric values, indicating the lower and upper bounds of standard deviation of #' log2 count matrix from the tumor samples to subset. Default is c(0.2, 0.8) #' @return A subset of the count matrix #' #' @export subset_sd subset_sd <- function(count.matrix, normal.id, tumor.id, cutoff_normal = c(0.1, 0.6), cutoff_tumor = c(0.2, 0.8)){ count.matrix[which(count.matrix == 0, arr.ind = T)] = 1 sdn.obs <- apply(log2(count.matrix[, match(normal.id, colnames(count.matrix))]), 1, sd) sdm.obs <- apply(log2(count.matrix[, match(tumor.id, colnames(count.matrix))]), 1, sd) indx <- which(sdn.obs > cutoff_normal[1] & sdn.obs < cutoff_normal[2] & sdm.obs > cutoff_tumor[1] & sdm.obs < cutoff_tumor[2]) count.matrix.subset <- count.matrix[indx, ] return(count.matrix.subset) } #' @title plot_dim #' @description Plot the distribution of tumor and normal samples in a 2D PCA space based on their expressions #' @name plot_dim #' @rdname detect_suspicious_sample_by_hierarchical_clustering_2comp #' @param count.matrix A matrix of raw expression count with \eqn{G} by \eqn{(My + M1)}, where \eqn{G} is the number #' of genes, \eqn{My} is the number of mixed samples and \eqn{M1} is the number of normal samples. Row names are genes #' column names are sample ids. #' @param normal.id A vector of normal sample ids #' @param tumor.id A vector of tumor sample ids #' @param legend.position Position of legend in the plot. Default is bottomleft. #' @param legend.cex Character expansion factor relative to current par("cex"). Default = 1.2 #' @return #' #' @export plot_dim plot_dim <- function(count.matrix, labels, legend.position = 'bottomleft', legend.cex = 1.2){ if(length(labels) != ncol(count.matrix)){ stop("The length of labels must be the same with the numbe of columns in count.matrix.") } N.label <- length(unique(labels)) count.matrix[which(count.matrix == 0, arr.ind = T)] = 1 ## PCA dimension reduction res = 0 res <- prcomp(t(log2(count.matrix)), center = T, scale. = T) ## PC variance pct1 = round(res$sdev[1]/sum(res$sdev), 3)*100 pct2 = round(res$sdev[2]/sum(res$sdev), 3)*100 ## Dimension dim1 = res$x[,1]; dim2 = res$x[,2] ## x/ylab text xlab.text = paste("First Comp: ", as.character(pct1), "% variance", sep="") ylab.text = paste("Second Comp: ", as.character(pct2), "% variance", sep="") main.text = 'PCA' ## Plot plot(dim1, dim2, cex = 1, xlab=xlab.text, ylab=ylab.text, main = main.text, col=rainbow(N.label)[as.numeric(factor(labels))], pch = as.numeric(factor((labels))), lwd=1.5) abline(h=0, v=0, col="brown", lty=2) abline(h=0, v=0, col="brown", lty=2) center1<-tapply(dim1, factor(labels), mean) center2<-tapply(dim2, factor(labels), mean) ## add lines and centers for (ii in 1:length(center1)) { groupi<-cbind(dim1,dim2)[as.numeric(factor(labels))==ii, 1:2] if (class(groupi)[1] =="matrix") { for (j in (1:nrow(groupi))) { segments(groupi[j,1], groupi[j,2], center1[ii], center2[ii], col = rainbow(N.label)[ii], lwd = 0.5) } }else { segments(groupi[1], groupi[2], center1[ii], center2[ii], col = rainbow(N.label)[ii], lwd = 0.5) } } points(center1, center2, pch = 7, lwd = 1.5, col = rainbow(N.label)) legend(legend.position, legend=names(table(factor(labels))), text.col=rainbow(N.label), pch=1:N.label, col=rainbow(N.label), lty=1, cex = legend.cex) } #' @title scale_normalization_75th_percentile #' @description Quantile normalization for the raw count matrix of tumor and normal reference using the 0.75 quantile scale normalization #' @name scale_normalization_75th_percentile #' @param count.matrix A matrix of raw expression count with \eqn{G} by \eqn{(My + M1)}, where \eqn{G} is the number #' of genes, \eqn{My} is the number of mixed samples and \eqn{M1} is the number of normal samples. Row names are genes #' column names are sample ids. #' @return the scale normalized count matrix #' #' @export scale_normalization_75th_percentile scale_normalization_75th_percentile <- function(count.matrix){ newt <- count.matrix colnames(newt) = NULL rownames(newt) = NULL designs=c(rep("0", dim(count.matrix)[2])) seqData=newSeqCountSet(as.matrix(newt), designs) # quantile normalization/total/median ###try different normalization method### seqData=estNormFactors(seqData, "quantile") k3=seqData@normalizationFactor mk3=median(k3) k3=k3/mk3 temp<-newt for(i in 1:ncol(newt)){ temp[,i] = temp[,i]/k3[i] } count.matrix.normalized<-temp colnames(count.matrix.normalized)<-colnames(count.matrix) rownames(count.matrix.normalized)<-rownames(count.matrix) return(count.matrix.normalized) } #' @title subset_sd_gene_remaining #' @description Find the cutoffs to filter out genes with large standard deviations of log2 expressions in both normal and tumor samples #' @name subset_sd_gene_remaining #' @param count.matrix A matrix of raw expression count with \eqn{G} by \eqn{(My + M1)}, where \eqn{G} is the number #' of genes, \eqn{My} is the number of mixed samples and \eqn{M1} is the number of normal samples. Row names are genes #' column names are sample ids. #' @param normal.id A vector of normal sample ids #' @param tumor.id A vector of tumor sample ids #' @param cutoff_normal_range A vector of two numeric values, indicating the lower and upper bounds of standard deviation of #' log2 count matrix from the normal samples to subset. Default is c(0.2, 0.6) #' @param cutoff_tumor_range A vector of two numeric values, indicating the lower and upper bounds to search standard deviation of #' log2 count matrix from the normal samples to subset. Default is c(0.2, 0.6) #' @param cutoff_step A scatter value indicating the step size of changing cutoff_normal_range and cutoff_tumor_range to find a #' suitable subset of count matrix for downstream analysis #' #' @export subset_sd_gene_remaining subset_sd_gene_remaining <- function(count.matrix, normal.id, tumor.id, cutoff_normal_range = c(0.2, 0.6), cutoff_tumor_range = c(0.2, 0.8), cutoff_step = 0.2){ if(length(normal.id) + length(tumor.id) != ncol(count.matrix)){ stop("Total number of normal and tumor samples in normal.id and tumor.id must be the same with the numbe of columns in count.matrix.") } count.matrix[which(count.matrix == 0, arr.ind = T)] = 1 sdn.obs <- apply(log2(count.matrix[, match(normal.id, colnames(count.matrix))]), 1, sd) sdm.obs <- apply(log2(count.matrix[, match(tumor.id, colnames(count.matrix))]), 1, sd) cutoff_normal_range <- seq(cutoff_normal_range[1], cutoff_normal_range[2], by = cutoff_step) cutoff_tumor_range <- seq(cutoff_tumor_range[1], cutoff_tumor_range[2], by = cutoff_step) num_gene_remaining_different_cutoffs <- NULL for(cutoff_normal in cutoff_normal_range){ if(cutoff_normal == cutoff_normal_range[1]) next for(cutoff_tumor in cutoff_tumor_range){ if(cutoff_tumor == cutoff_tumor_range[1]) next indx <- which(sdn.obs > cutoff_normal_range[1] & sdn.obs < cutoff_normal & sdm.obs > cutoff_tumor_range[1] & sdm.obs < cutoff_tumor) num_gene_remaining_different_cutoffs <- rbind(num_gene_remaining_different_cutoffs, data.frame(normal.cutoff.low=cutoff_normal_range[1], normal.cutoff.high=cutoff_normal, tumor.cutoff.low=cutoff_tumor_range[1], tumor.cutoff.high=cutoff_tumor, num.gene.remaining=length(indx))) } } return(num_gene_remaining_different_cutoffs) } #' @title DeMixT_preprocessing #' @description DeMixT preprocessing in one go #' @name DeMixT_preprocessing #' @param count.matrix A matrix of raw expression count with \eqn{G} by \eqn{(My + M1)}, where \eqn{G} is the number #' of genes, \eqn{My} is the number of mixed samples and \eqn{M1} is the number of normal samples. Row names are genes #' column names are sample ids. #' @param normal.id A vector of normal sample ids #' @param tumor.id A vector of tumor sample ids #' @param selected.genes A integer number indicating the number of genes selected before running DeMixT with the GS (Gene Selection) method #' @param cutoff_normal_range A vector of two numeric values, indicating the lower and upper bounds of standard deviation of #' log2 count matrix from the normal samples to subset. Default is c(0.2, 0.6) #' @param cutoff_tumor_range A vector of two numeric values, indicating the lower and upper bounds to search standard deviation of #' log2 count matrix from the normal samples to subset. Default is c(0.2, 0.6) #' @param cutoff_step A scatter value indicating the step size of changing cutoff_normal_range and cutoff_tumor_range to find a #' suitable subset of count matrix for downstream analysis #' #' @return processed count matrix #' #' @export DeMixT_preprocessing DeMixT_preprocessing <- function(count.matrix, normal.id, tumor.id, selected.genes = 9000, cutoff_normal_range=c(0.1, 1.0), cutoff_tumor_range=c(0, 2.5), cutoff_step=0.2){ stopifnot(cutoff_normal_range[1] >= 0) stopifnot(cutoff_tumor_range[1] >= 0) stopifnot(cutoff_normal_range[2] >= cutoff_normal_range[0]) stopifnot(cutoff_tumor_range[2] >= cutoff_tumor_range[0]) stopifnot(cutoff_step > 0) stopifnot(selected.genes > 0) ##stopifnot(is.integer(selected.genes)) num_gene_remaining_different_cutoffs <- subset_sd_gene_remaining(count.matrix, normal.id, tumor.id, cutoff_normal_range, cutoff_tumor_range, cutoff_step) num_gene_remaining_different_cutoffs_filter <- num_gene_remaining_different_cutoffs[which(num_gene_remaining_different_cutoffs$num.gene.remaining - selected.genes > 0), ] num_gene_remaining_different_cutoffs_filter <- num_gene_remaining_different_cutoffs_filter[order(num_gene_remaining_different_cutoffs_filter$num.gene.remaining), ] sd_cutoff_normal <- c(num_gene_remaining_different_cutoffs_filter$normal.cutoff.low[1], num_gene_remaining_different_cutoffs_filter$normal.cutoff.high[1]) sd_cutoff_tumor <- c(num_gene_remaining_different_cutoffs_filter$tumor.cutoff.low[1], num_gene_remaining_different_cutoffs_filter$tumor.cutoff.high[1]) count.matrix <- subset_sd(count.matrix, normal.id, tumor.id, cutoff_normal = sd_cutoff_normal, cutoff_tumor = sd_cutoff_tumor) count.matrix = scale_normalization_75th_percentile(count.matrix) preprocessing_output <- list() preprocessing_output[['count.matrix']] <- count.matrix preprocessing_output[["sd_cutoff_normal"]] <- sd_cutoff_normal preprocessing_output[["sd_cutoff_tumor"]] <- sd_cutoff_tumor return(preprocessing_output) } #' @title batch_correction #' @description Batch effect correction for multiple batches of tumor samples using ComBat #' @name batch_correction #' @param count.matrix A matrix of raw expression count with \eqn{G} by \eqn{(My)}, where \eqn{G} is the number #' of genes, \eqn{My} is the number of mixed tumor samples. Row names are genes #' column names are tumor sample ids. #' @param batch_labels Factor of tumor samples from different batches #' @return Batch effect corrected count matrix for tumor samples #' #' @export batch_correction batch_correction <- function(count.matrix, batch_labels){ if(length(batch_labels) != ncol(count.matrix)){ stop("Total number of normal and tumor samples in normal.id and tumor.id must be the same with the numbe of columns in count.matrix.") } count.matrix[which(count.matrix == 0, arr.ind = T)] = 1 count.matrix.combat.log2 = ComBat(dat = log2(count.matrix), batch = batch_labels, mod = NULL, par.prior=TRUE, mean.only = F) count.matrix.combat.combat <- 2^count.matrix.combat.log2 return(count.matrix.combat.combat) } DeMixT/R/DeMixT_S2.R0000644000175200017520000002103514710233064014751 0ustar00biocbuildbiocbuild#' Deconvolves expressions of each individual sample for unknown component #' #' This function is designed to estimate the deconvolved expressions of #' individual mixed tumor samples for unknown component for each gene. #' #' #' @param data.Y A SummarizedExperiment object of expression data from mixed #' tumor samples. It is a \eqn{G} by \eqn{My} matrix where \eqn{G} is the #' number of genes and \eqn{My} is the number of mixed samples. Samples with #' the same tissue type should be placed together in columns. #' @param data.N1 A SummarizedExperiment object of expression data from #' reference component 1 (e.g., normal). It is a \eqn{G} by \eqn{M1} matrix #' where \eqn{G} is the number of genes and \eqn{M1} is the number of samples #' for component 1. #' @param data.N2 A SummarizedExperiment object of expression data from #' additional reference samples. It is a \eqn{G} by \eqn{M2} matrix where #' \eqn{G} is the number of genes and \eqn{M2} is the number of samples for #' component 2. Component 2 is needed only for running a three-component model. #' @param givenpi A vector of proportions for all mixed tumor samples. In #' two-component analysis, it gives the proportions of the unknown reference #' component, and in three-component analysis, it gives the proportions for the #' two known components. #' @param nbin Number of bins used in numerical integration for computing #' complete likelihood. A larger value increases accuracy in estimation but #' increases the running time, especially in a three-component deconvolution #' problem. The default is 50. #' @param nthread The number of threads used for deconvolution when OpenMP is #' available in the system. The default is the number of whole threads minus #' one. In our no-OpenMP version, it is set to 1. #' @return \item{decovExprT}{A matrix of deconvolved expression profiles #' corresponding to T-component in mixed samples for a given subset of genes. #' Each row corresponds to one gene and each column corresponds to one sample.} #' \item{decovExprN1}{A matrix of deconvolved expression profiles corresponding #' to N1-component in mixed samples for a given subset of genes. Each row #' corresponds to one gene and each column corresponds to one sample.} #' \item{decovExprN2}{A matrix of deconvolved expression profiles corresponding #' to N2-component in mixed samples for a given subset of genes in a #' three-component setting. Each row corresponds to one gene and each column #' corresponds to one sample.} \item{decovMu}{A matrix of estimated \eqn{Mu} of #' log2-normal distribution for both known (\eqn{MuN1, MuN2}) and unknown #' component (\eqn{MuT}). Each row corresponds to one gene.} #' \item{decovSigma}{Estimated \eqn{Sigma} of log2-normal distribution for both #' known (\eqn{SigmaN1, SigmaN2}) and unknown component (\eqn{SigmaT}). Each #' row corresponds to one gene.} #' @author Zeya Wang, Wenyi Wang #' @seealso http://bioinformatics.mdanderson.org/main/DeMixT #' @references Wang Z, Cao S, Morris J S, et al. Transcriptome Deconvolution of #' Heterogeneous Tumor Samples with Immune Infiltration. iScience, 2018, 9: #' 451-460. #' @keywords DeMixT_S2 #' @examples #' #' # Example 1: two-component deconvolution given proportions #' data(test.data.2comp) #' givenpi <- c(t(as.matrix(test.data.2comp$pi[-2,]))) #' res.S2 <- DeMixT_S2(data.Y = test.data.2comp$data.Y, #' data.N1 = test.data.2comp$data.N1, #' data.N2 = NULL, #' givenpi = givenpi, #' nbin = 50) #' # #' # Example 2: three-component deconvolution given proportions #' # data(test.data.3comp) #' # givenpi = c(t(test.data.3comp$pi[-3,])) #' # res <- DeMixT_S2(data.Y = test.data.3comp$data.Y, #' # data.N1 = test.data.3comp$data.N1, #' # data.N2 = test.data.3comp$data.N2, #' # givenpi = givenpi, #' # nbin = 50) #' #' #' @export DeMixT_S2 DeMixT_S2 <- function(data.Y, data.N1, data.N2 = NULL, givenpi, nbin = 50, nthread = parallel::detectCores() - 1) { filter.out = TRUE filter.option = 1 data.Y <- SummarizedExperiment::assays(data.Y)[[1]] data.N1 <- SummarizedExperiment::assays(data.N1)[[1]] if (!is.null(data.N2)){ data.N2 <- SummarizedExperiment::assays(data.N2)[[1]] } if (is.null(rownames(data.Y))) { rownames(data.Y) <- paste('Gene', seq = seq(1, nrow(data.Y))) rownames(data.N1) <- paste('Gene', seq = seq(1, nrow(data.N1))) if(!is.null(data.N2)){ rownames(data.N2) <- paste('Gene', seq = seq(1, nrow(data.N2))) } } if (is.null(colnames(data.Y))) { colnames(data.Y) <- paste('Sample', seq = seq(1, ncol(data.Y))) colnames(data.N1) <- paste('Sample', seq = seq(1, ncol(data.N1))) if(!is.null(data.N2)){ colnames(data.N2) <- paste('Sample', seq = seq(1, ncol(data.N2))) } } sample.id <- colnames(data.Y) if (is.null(data.N2)) { inputdata <- cbind(data.N1, data.Y) groupid <- c(rep(1, ncol(data.N1)), rep(3, ncol(data.Y))) }else { inputdata <- cbind(data.N1, data.N2, data.Y) groupid <- c(rep(1, ncol(data.N1)), rep(2, ncol(data.N2)), rep(3, ncol(data.Y))) } if (filter.option == 1) { index <- apply(inputdata, 1, function(x) sum(x <= 0) == 0) inputdata <- inputdata[index, ] data.N1 <- data.N1[index, ] if (!is.null(data.N2)) data.N2 <- data.N2[index, ] data.Y <- data.Y[index, ] }else if (filter.option == 2) { inputdata <- inputdata + 1 data.N1 <- data.N1 + 1 if (!is.null(data.N2)) data.N2 <- data.N2 + 1 data.Y <- data.Y + 1 }else { stop("The argument filter.option can only be 1 or 2") } if (is.null(data.N2)) { if (dim(inputdata)[1] == 1) { inputdata <- t(as.matrix(inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1), ])) } else { inputdata <- inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1), ] } }else { if (dim(inputdata)[1] == 1) { inputdata <- t(as.matrix(inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1) & apply(data.N2, 1, function(x) length(unique(x)) > 1), ])) } else { inputdata <- inputdata[apply(data.N1, 1, function(x) length(unique(x)) > 1) & apply(data.N2, 1, function(x) length(unique(x)) > 1), ] } } gene.id <- rownames(inputdata) inputdata <- as.matrix(inputdata) res <- Optimum_KernelC(inputdata, groupid, setting.pi = 1, nspikein = 0, givenpi = givenpi, givenpiT = rep(0, ncol(data.Y)), niter = 1, ninteg = nbin, tol = 1e-05, nthread = nthread) decovExprT <- res$decovExprT decovExprT[which(decovExprT < 0,arr.ind = TRUE)] <- 0 colnames(decovExprT) <- sample.id row.names(decovExprT) <- gene.id decovExprN1 <- res$decovExprN1 decovExprN1[which(decovExprN1 < 0,arr.ind = TRUE)] <- 0 colnames(decovExprN1) <- sample.id row.names(decovExprN1) <- gene.id decovExprN2 <- res$decovExprN2 decovExprN2[which(decovExprN2 < 0,arr.ind = TRUE)] <- 0 colnames(decovExprN2) <- sample.id row.names(decovExprN2) <- gene.id if (is.null(data.N2)) { decovMuN <- rowMeans(log2(data.N1[gene.id, ])) decovSigmaN <- apply(log2(data.N1[gene.id, ]), 1 , sd) decovMu <- cbind(decovMuN, res$decovMu) colnames(decovMu) <- c("MuN1", "MuT") row.names(decovMu) <- gene.id decovSigma <- cbind(decovSigmaN, res$decovSigma) colnames(decovSigma) <- c("SigmaN1", "SigmaT") row.names(decovSigma) <- gene.id filter.in <- rep(TRUE, nrow(inputdata)) if (filter.out == TRUE) filter.in <- (decovMuN - res$decovMu <= 4) return(list(decovExprT = decovExprT[filter.in, ], decovExprN1 = decovExprN1[filter.in, ], decovExprN2 = decovExprN2[filter.in, ], decovMu = decovMu[filter.in, ], decovSigma = decovSigma[filter.in, ])) }else { decovMuN1 <- rowMeans(log2(data.N1[gene.id,])) decovSigmaN1 <- apply(log2(data.N1[gene.id,]), 1, sd) decovMuN2 <- rowMeans(log2(data.N2[gene.id,])) decovSigmaN2 <- apply(log2(data.N2[gene.id,]), 1, sd) decovMu <- cbind(decovMuN1, decovMuN2, res$decovMu) colnames(decovMu) <- c("MuN1", "MuN2", "MuT") row.names(decovMu) <- gene.id decovSigma <- cbind(decovSigmaN1, decovSigmaN2, res$decovSigma) colnames(decovSigma) <- c("SigmaN1", "SigmaN2", "SigmaT") row.names(decovSigma) <- gene.id return(list(decovExprT = decovExprT, decovExprN1 = decovExprN1, decovExprN2 = decovExprN2, decovMu = decovMu, decovSigma = decovSigma)) } } DeMixT/R/Optimum_KernelC.R0000644000175200017520000003012714710233064016312 0ustar00biocbuildbiocbuild#' Kernel function for optimizing parameters and hidden variables in DeMixT #' #' This function is invoked by DeMixT_GS or DeMixT_DE and DeMixT_S2 to finish #' parameter estimation by iterated conditional mode algorithm and reconstitute #' gene expression profile of all components. #' #' #' @param inputdata A matrix of expression data (e.g gene expressions) from #' reference (e.g. normal) and mixed samples (e.g. mixed tumor samples). It is #' a \eqn{G*M} matrix where \eqn{G} is the number of genes and \eqn{M} is the #' number of samples including reference and mixed samples. Samples with the #' same tissue type should be placed together in columns (e.g. cbind(normal #' amples, mixed tumor samples). #' @param groupid A vector of indicators to denote if the corresponding samples #' are reference samples or mixed tumor samples. DeMixT is able to deconvolve #' mixed tumor samples with at most three components. We use 1 and 2 to denote #' the samples referencing the first and the second known component in mixed #' tumor samples. We use 3 to indicate mixed tumor samples prepared to be #' deconvolved. For example, in two-component deconvolution, we have #' c(1,1,...,3,3) and in three-component deconvolution, we have #' c(1,1,...,2,2,...,3,3). #' @param nspikein The number of spikes in normal reference used for proportion #' estimation. The default value is \eqn{ min(200, 0.3*My)}, where \eqn{My} the #' number of mixed tumor samples. If it is set to 0, proportion estimation is #' performed without any spike in normal reference. #' @param setting.pi If it is set to 0, then deconvolution is performed without #' any given proportions; if set to 1, deconvolution with given proportions for #' the first and the second known component is run; if set to 2, deconvolution #' is run with given tumor proportions. This option helps to perform #' deconvolution in different settings. In estimation of component-specific #' proportions, we use a subset of genes ; so when it is required to deconvolve #' another subset of genes, we just easily plug back our estimated proportions #' by setting this option to 1. In our two-step estimation strategy in a #' three-component setting, this option is set to 2 to implement the second #' step. #' @param givenpi \eqn{ST}-Vector of proportions. Given the number of mixed #' tumor samples is \eqn{My(My 1) groupid2 = which(groupid == 2) groupidT = which(groupid == 3) sample.id = colnames(inputdata[,groupidT]) gene.id = row.names(inputdata) if(nCid == 1){ inputdata = cbind(inputdata[, groupid1], inputdata[, groupidT]) groupid = c(rep(1, length(groupid1)), rep(3, length(groupidT))) }else if(nCid == 2){ inputdata = cbind(inputdata[, groupid1], inputdata[, groupid2], inputdata[, groupidT]) groupid = c(rep(1, length(groupid1)), rep(2, length(groupid2)), rep(3, length(groupidT))) } dataarray1 <- (as.array(matrix(inputdata, nrow = 1, byrow = FALSE))) ## expression profile of the normal sample as an array ## Set given proportions if(setting.pi == 1){ if(!is.vector(givenpi)) stop("argument option must be a vector if pi is known"); givenpi=(as.array(givenpi)) givenpi3=(as.array(rep(0, intx))) }else if(setting.pi == 2){ givenpi=(as.array(rep(0, nCid*intx))) givenpi3 = as.array(givenpiT) }else if(setting.pi==0){ givenpi=(as.array(rep(0, nCid*intx))) givenpi3=(as.array(rep(0, intx))) }else{ stop("setting.pi argument must be set 0, 1 or 2") } if(nCid == 1){ givenpi1 = givenpi[seq(1,intx)] givenpi2 = (as.array(rep(0, intx))) }else{ givenpi1 = givenpi[seq(1,intx)] givenpi2 = givenpi[(intx+1):(2*intx)] } s0 = sg0 + 0.0 m0 = mu0 + 0.0 ## Initilize pi if(nCid == 1){ if(is.null(pi01)){ pi01 = pi02 = array(0 , c(1, intx)) for(s in 1:intx){ pi01[s] = runif(1, min = 0.01, max = 0.98) } colnames(pi01) <- paste('Sample', seq = seq(1:intx)) rownames(pi01) <- paste('PiN1') cat("Initial of Proportions:\n") print(round(t(pi01),4)) } }else if(nCid == 2){ if(is.null(pi01) | is.null(pi02)){ pi01 = pi02 = array(0 , c(1, intx)) for(s in 1:intx){ pi01[s] = runif(1, min = 0.01, max = 0.97) pi02[s] = runif(1, min = 0.01, max = 0.98 - pi01[s]) } # cat('Initial pi1 is \n', pi01, '\n') # cat('Initial pi2 is \n', pi02, '\n') } } rres <- .C("Tdemix", dataarray1, as.integer(groupid), as.integer(nsub), as.integer(wgenes), as.integer(nspikein), as.integer(setting.pi), pi01, pi02, givenpi1, givenpi2, givenpi3, as.integer(nCid), as.integer(niter), as.integer(ninteg), tol, as.integer(nthread), s0, m0, rep(0, 2 * intx), rep(0, intx * wgenes), rep(0, niter * wgenes), rep(0, niter * wgenes), rep(0, niter * intx), rep(0, niter * intx), rep(0,niter), rep(0, intx * wgenes), rep(0, intx * wgenes)) obj<-rres[[25]] if(sum(obj == 0)>1){ niter1 <- which(obj==0)[1]-1 }else{ niter1 <- length(obj) } if (setting.pi != 1) { message('Objective function in each step: ') message(paste(obj[seq(1,niter1)], " ")) message(' \n') } outcome1<-matrix(rres[[19]], ncol=intx, nrow=2, byrow=TRUE) outcome2<-matrix(rres[[20]], ncol=(intx), nrow=wgenes, byrow = TRUE) outcome3<-matrix(rres[[21]], ncol=niter, nrow=wgenes,byrow= TRUE) outcome4<-matrix(rres[[22]], ncol=niter, nrow=wgenes,byrow=TRUE) outcome5<-matrix(rres[[23]], ncol=niter, nrow=intx,byrow=TRUE) outcome6<-matrix(rres[[24]], ncol=niter, nrow=intx,byrow=TRUE) outcome21<-matrix(rres[[26]], ncol=(intx), nrow=wgenes, byrow = TRUE) outcome22<-matrix(rres[[27]], ncol=(intx), nrow=wgenes, byrow = TRUE) # if(setting.pi == 1){ # print(paste0('Totally ', round(100*sum(outcome4[,niter]>99.99)/wgenes,3),'% genes estimated touch the optimization bound')) # }else{ # print(paste0('Totally ', round(100*sum(outcome4[,niter]>24.99)/wgenes,3),'% genes estimated touch the optimization bound')) # } outcome4 <- sqrt(outcome4) return(list(obj_val = obj[niter1], obj_iter=obj, pi = outcome1, decovExprT = outcome2, decovExprN1 = outcome21, decovExprN2 = outcome22, decovMu = as.matrix(outcome3[,seq(1,niter1)]), decovSigma = as.matrix(outcome4[,seq(1,niter1)]), pi1 = as.matrix(outcome5[,seq(1,niter1)]), pi2 = as.matrix(outcome6[,seq(1,niter1)]))) } DeMixT/R/RcppExports.R0000644000175200017520000002331714710233064015551 0ustar00biocbuildbiocbuild# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 ft_y <- function(y, mung, mutg, sng, stg, pi1, pi2) { .Call(`_DeMixT_ft_y`, y, mung, mutg, sng, stg, pi1, pi2) } pf_y <- function(y, samp, mung, mutg, sng, stg, pi1) { .Call(`_DeMixT_pf_y`, y, samp, mung, mutg, sng, stg, pi1) } pmin_y <- function(ax, bx, samp, y, mung, mutg, sng, stg, tol) { .Call(`_DeMixT_pmin_y`, ax, bx, samp, y, mung, mutg, sng, stg, tol) } tf_y <- function(genes, mu, sigma, y, pi1, mung, sng) { .Call(`_DeMixT_tf_y`, genes, mu, sigma, y, pi1, mung, sng) } tmin_y2 <- function(ax, bx, genes, mu, y, pi1, mung, sng, tol) { .Call(`_DeMixT_tmin_y2`, ax, bx, genes, mu, y, pi1, mung, sng, tol) } mint <- function(genes, mu, y, pi1, mung, sng) { .Call(`_DeMixT_mint`, genes, mu, y, pi1, mung, sng) } tmin_y <- function(ax, bx, genes, y, pi1, mung, sng, tol) { .Call(`_DeMixT_tmin_y`, ax, bx, genes, y, pi1, mung, sng, tol) } Loglikelihood_ft_y <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_Loglikelihood_ft_y`, y, Pi, MuN, MuT, SigmaN, SigmaT) } SoftThreshold <- function(x, t) { .Call(`_DeMixT_SoftThreshold`, x, t) } SoftThreshold_vec <- function(x, t) { .Call(`_DeMixT_SoftThreshold_vec`, x, t) } Gt <- function(x, Dg, t) { .Call(`_DeMixT_Gt`, x, Dg, t) } Gt_vec <- function(x, Dg, t) { .Call(`_DeMixT_Gt_vec`, x, Dg, t) } Alpha_search_2D <- function(x, p, S, G) { .Call(`_DeMixT_Alpha_search_2D`, x, p, S, G) } Alpha_search_Pi_2D <- function(x, p, S) { .Call(`_DeMixT_Alpha_search_Pi_2D`, x, p, S) } Alpha_search_MuT_2D <- function(x, p, G) { .Call(`_DeMixT_Alpha_search_MuT_2D`, x, p, G) } Alpha_search_SigmaT_2D <- function(x, p, G) { .Call(`_DeMixT_Alpha_search_SigmaT_2D`, x, p, G) } Alpha_search_2D_sigma <- function(x, p, S, G) { .Call(`_DeMixT_Alpha_search_2D_sigma`, x, p, S, G) } Unit1 <- function(t, Mu, Pit) { .Call(`_DeMixT_Unit1`, t, Mu, Pit) } x_update_2D <- function(Pi, MuT, SigmaT, S, G) { .Call(`_DeMixT_x_update_2D`, Pi, MuT, SigmaT, S, G) } x_update_inv_2D <- function(x, S, G) { .Call(`_DeMixT_x_update_inv_2D`, x, S, G) } innerCPP_2D <- function(t, y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_innerCPP_2D`, t, y, Pi, MuN, MuT, SigmaN, SigmaT) } inner_trapez_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_inner_trapez_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } DPi_inner_2D <- function(t, y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_DPi_inner_2D`, t, y, Pi, MuN, MuT, SigmaN, SigmaT) } DPi_outer_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_DPi_outer_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D1f0Pi_func_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D1f0Pi_func_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } DMuT_inner_2D <- function(t, y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_DMuT_inner_2D`, t, y, Pi, MuN, MuT, SigmaN, SigmaT) } DMuT_outer_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_DMuT_outer_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D1f0MuT_func_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D1f0MuT_func_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } DSigmaT_inner_2D <- function(t, y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_DSigmaT_inner_2D`, t, y, Pi, MuN, MuT, SigmaN, SigmaT) } DSigmaT_outer_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_DSigmaT_outer_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D1f0SigmaT_func_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D1f0SigmaT_func_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2Pi_inner_2D <- function(t, y, Pi, MuN, MuT, SigmaN, SigmaT, ln2) { .Call(`_DeMixT_D2Pi_inner_2D`, t, y, Pi, MuN, MuT, SigmaN, SigmaT, ln2) } D2Pi_outer_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2Pi_outer_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2f0Pi_func_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2f0Pi_func_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2PiMuT_inner_2D <- function(t, y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2PiMuT_inner_2D`, t, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2PiMuT_outer_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2PiMuT_outer_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2f0PiMuT_func_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2f0PiMuT_func_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2PiSigmaT_inner_2D <- function(t, y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2PiSigmaT_inner_2D`, t, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2PiSigmaT_outer_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2PiSigmaT_outer_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2f0PiSigmaT_func_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2f0PiSigmaT_func_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2MuT_inner_2D <- function(t, y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2MuT_inner_2D`, t, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2MuT_outer_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2MuT_outer_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2f0MuT_func_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2f0MuT_func_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2MuTSigmaT_inner_2D <- function(t, y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2MuTSigmaT_inner_2D`, t, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2MuTSigmaT_outer_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2MuTSigmaT_outer_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2f0MuTSigmaT_func_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2f0MuTSigmaT_func_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2SigmaT_inner_2D <- function(t, y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2SigmaT_inner_2D`, t, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2SigmaT_outer_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2SigmaT_outer_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2f0SigmaT_func_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2f0SigmaT_func_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } f0_func_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_f0_func_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } Loglikelihood_Pi_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT, j) { .Call(`_DeMixT_Loglikelihood_Pi_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT, j) } GoldenSection_Loglikelihood_Pi_2D <- function(y, MuN, MuT, SigmaN, SigmaT, j) { .Call(`_DeMixT_GoldenSection_Loglikelihood_Pi_2D`, y, MuN, MuT, SigmaN, SigmaT, j) } Loglikelihood_MuT_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT, i) { .Call(`_DeMixT_Loglikelihood_MuT_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT, i) } GoldenSection_Loglikelihood_MuT_2D <- function(y, Pi, MuN, SigmaN, SigmaT, j) { .Call(`_DeMixT_GoldenSection_Loglikelihood_MuT_2D`, y, Pi, MuN, SigmaN, SigmaT, j) } Loglikelihood_SigmaT_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT, i) { .Call(`_DeMixT_Loglikelihood_SigmaT_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT, i) } GoldenSection_Loglikelihood_SigmaT_2D <- function(y, Pi, MuN, MuT, SigmaN, j) { .Call(`_DeMixT_GoldenSection_Loglikelihood_SigmaT_2D`, y, Pi, MuN, MuT, SigmaN, j) } Loglikelihood_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_Loglikelihood_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } Loglikelihood_2D_L1 <- function(y, Pi, MuN, Beta, SigmaN, SigmaT, lambda) { .Call(`_DeMixT_Loglikelihood_2D_L1`, y, Pi, MuN, Beta, SigmaN, SigmaT, lambda) } D1Loglikelihood_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D1Loglikelihood_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D1Pi_Loglikelihood_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D1Pi_Loglikelihood_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D1MuTSigmaT_Loglikelihood_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D1MuTSigmaT_Loglikelihood_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D1MuT_Loglikelihood_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D1MuT_Loglikelihood_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D1SigmaT_Loglikelihood_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D1SigmaT_Loglikelihood_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } log_divide <- function(x, f) { .Call(`_DeMixT_log_divide`, x, f) } D1Loglikelihood_log_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D1Loglikelihood_log_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2Loglikelihood_log_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2Loglikelihood_log_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2Loglikelihood_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2Loglikelihood_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } D2Loglikelihood_unit_2D <- function(y, Pi, MuN, MuT, SigmaN, SigmaT) { .Call(`_DeMixT_D2Loglikelihood_unit_2D`, y, Pi, MuN, MuT, SigmaN, SigmaT) } Gfunc_2D_C <- function(alpha, x, p, S, G, y, MuN, SigmaN) { .Call(`_DeMixT_Gfunc_2D_C`, alpha, x, p, S, G, y, MuN, SigmaN) } GoldenLine_search_2D <- function(alpha, x, p, y, MuN, SigmaN, S, G, steps) { .Call(`_DeMixT_GoldenLine_search_2D`, alpha, x, p, y, MuN, SigmaN, S, G, steps) } GoldenLine_search_Pi_2D <- function(alpha, x, p, y, MuN, MuT, SigmaN, SigmaT, steps) { .Call(`_DeMixT_GoldenLine_search_Pi_2D`, alpha, x, p, y, MuN, MuT, SigmaN, SigmaT, steps) } GoldenLine_search_MuT_2D <- function(alpha, x, p, y, Pi, MuN, SigmaN, SigmaT, steps) { .Call(`_DeMixT_GoldenLine_search_MuT_2D`, alpha, x, p, y, Pi, MuN, SigmaN, SigmaT, steps) } GoldenLine_search_SigmaT_2D <- function(alpha, x, p, y, Pi, MuN, MuT, SigmaN, steps) { .Call(`_DeMixT_GoldenLine_search_SigmaT_2D`, alpha, x, p, y, Pi, MuN, MuT, SigmaN, steps) } DeMixT/R/simulate_2comp.R0000644000175200017520000001273214710233064016202 0ustar00biocbuildbiocbuild#' Function to simulate two-component test data #' #' Function to simulate two-component test data for DeMixT. #' #' #' @param G Number of genes for simulation. #' @param My Number of mixture tumor samples for simulation. #' @param M1 Number of normal reference for simulation. #' @param output.more.info The logical flag indicating wheter to show #' True.data.T and True.data.N1 in the output. The default is FALSE. #' @return \item{pi}{A matrix of estimated proportion. First row and second row #' corresponds to the proportion estimate for the known components and unkown #' component respectively for two or three component settings. Each column #' corresponds to one sample.} \item{Mu}{Simulated \eqn{Mu} of log2-normal #' distribution for both known (\eqn{MuN1}) and unknown component (\eqn{MuT}).} #' \item{Sigma}{Simulated \eqn{Sigma} of log2-normal distribution for both #' known (\eqn{SigmaN1}) and unknown component (\eqn{SigmaT}).} \item{data.Y}{A #' SummarizedExperiment object of expression data from mixed tumor samples. It #' is a \eqn{G} by \eqn{My} matrix where \eqn{G} is the number of genes and #' \eqn{My} is the number of mixed samples. Samples with the same tissue type #' should be placed together in columns.} \item{data.N1}{A SummarizedExperiment #' object of expression data from reference component 1 (e.g., normal). It is a #' \eqn{G} by \eqn{M1} matrix where \eqn{G} is the number of genes and \eqn{M1} #' is the number of samples for component 1.} \item{True.data.T}{A #' SummarizedExperiment object of simulated tumor expression data. It is a #' \eqn{G} by \eqn{My} matrix, where \eqn{G} is the number of genes and #' \eqn{My} is the number of mixed samples. This is enabled only when #' output.more.info = TRUE.} \item{True.data.N1}{A SummarizedExperiment object #' of simulated true expression data for reference component 1 (e.g., normal). #' It is a \eqn{G} by \eqn{M1} matrix where \eqn{G} is the number of genes and #' \eqn{M1} is the number of samples for component 1. This is enabled only when #' output.more.info = TRUE.} #' @examples #' #' test.data = simulate_2comp(G = 500, My = 100, M1 = 100) #' test.data$pi #' test.data$Mu #' test.data$Sigma #' #' @export simulate_2comp simulate_2comp <- function(G = 500, My = 100, M1 = 100, output.more.info = FALSE){ requireNamespace("truncdist", quietly=TRUE) requireNamespace("SummarizedExperiment", quietly=TRUE) ## Simulate MuN and MuT for each gene MuN <- rnorm(G, 7, 1.5) MuT <- rnorm(G, 7, 1.5) Mu <- cbind(MuN, MuT) colnames(Mu) <- c('MuN', 'MuT') rownames(Mu) <- paste('Gene', seq = seq(1, G)) ## Simulate SigmaN and SigmaT for each gene SigmaN <- runif(n = G, min = 0.1, max = 0.8) SigmaT <- runif(n = G, min = 0.1, max = 0.8) Sigma <- cbind(SigmaN, SigmaT) colnames(Sigma) <- c('SigmaN', 'SigmaT') rownames(Sigma) <- paste('Gene', seq = seq(1, G)) ## Initial values data.N1 <- matrix(0, G, M1) data.Y <- matrix(0, G, My) True.data.N1 <- matrix(0, G, My) True.data.T <- matrix(0, G, My) ## Creat row and column name rownames(data.N1) <- rownames(data.Y) <- rownames(True.data.N1) <- rownames(True.data.T) <- paste('Gene', seq = seq(1, G)) colnames(data.N1) <- paste('Sample', seq = seq(1, M1)) colnames(data.Y) <- colnames(True.data.N1) <- colnames(True.data.T) <- paste('Sample', seq = seq(1, My)) ## Simulate Tumor Proportion PiT = truncdist::rtrunc(n = My, spec = 'norm', mean = 0.55, sd = 0.2, a = 0.25, b = 0.95) pi <- rbind(1-PiT, PiT) rownames(pi) <- c('PiN', 'PiT') colnames(pi) <- paste('Sample', seq = seq(1, My)) ## Simulate Data for(k in 1:G){ data.N1[k,] <- 2^rnorm(M1, MuN[k], SigmaN[k]); # normal reference True.data.T[k,] <- 2^rnorm(My, MuT[k], SigmaT[k]); # True Tumor True.data.N1[k,] <- 2^rnorm(My, MuN[k], SigmaN[k]); # True Normal data.Y[k,] <- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.T[k,] # Mixture Tumor } ## Transfer into bioconductor format data.Y <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(data.Y)), rowData = DataFrame(row.names = rownames(data.Y)), colData = DataFrame(row.names = colnames(data.Y))) data.N1 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(data.N1)), rowData = DataFrame(row.names = rownames(data.N1)), colData = DataFrame(row.names = colnames(data.N1))) True.data.T <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(True.data.T)), rowData = DataFrame(row.names = rownames(True.data.T)), colData = DataFrame(row.names = colnames(True.data.T))) True.data.N1 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(True.data.N1)), rowData = DataFrame(row.names = rownames(True.data.N1)), colData = DataFrame(row.names = colnames(True.data.N1))) if(output.more.info){ test.data = list(pi=pi, Mu = Mu, Sigma = Sigma, data.Y = data.Y, data.N1 = data.N1, True.data.T = True.data.T, True.data.N1 = True.data.N1) }else{ test.data = list(pi=pi, Mu = Mu, Sigma = Sigma, data.Y = data.Y, data.N1 = data.N1) } return(test.data) } DeMixT/R/simulate_3comp.R0000644000175200017520000002113614710233064016201 0ustar00biocbuildbiocbuild#' Function to simulate three-component mixed cell line test data #' #' Function to simulate three-component mixed cell line test data used in #' DeMixT function. #' #' #' @param G1 Number of genes, where \eqn{\mu_{N1}} is close to \eqn{\mu_{N2}}. #' @param G2 Number of genes, where \eqn{\mu_{N1}} is not close to #' \eqn{\mu_{N2}}. #' @param My Number of mixture tumor samples for simulation. #' @param M1 Number of first known reference for simulation. #' @param M2 Number of second known reference for simulation. #' @param output.more.info The logical flag indicating wheter to show #' True.data.T, True.data.N1 and True.data.N2 in the output. The default is #' FALSE. #' @return \item{pi}{A matrix of estimated proportion. First row and second row #' corresponds to the proportion estimate for the known components and unkown #' component respectively for two or three component settings. Each column #' corresponds to one sample.} \item{Mu}{Simulated \eqn{Mu} of log2-normal #' distribution for both known (\eqn{MuN1, MuN2}) and unknown component #' (\eqn{MuT}).} \item{Sigma}{Simulated \eqn{Sigma} of log2-normal distribution #' for both known (\eqn{SigmaN1, SigmaN2}) and unknown component #' (\eqn{SigmaT}).} \item{data.Y}{A SummarizedExperiment object of simulated #' expression data from mixed tumor samples. It is a \eqn{G} by \eqn{My} matrix #' where \eqn{G} is the number of genes and \eqn{My} is the number of mixed #' samples. Samples with the same tissue type should be placed together in #' columns.} \item{data.N1}{A SummarizedExperiment object of simulated #' expression data from reference component 1 (e.g., normal). It is a \eqn{G} #' by \eqn{M1} matrix where \eqn{G} is the number of genes and \eqn{M1} is the #' number of samples for component 1.} \item{data.N2}{A SummarizedExperiment #' object of expression data from additional reference samples. It is a \eqn{G} #' by \eqn{M2} matrix where \eqn{G} is the number of genes and \eqn{M2} is the #' number of samples for component 2.} \item{True.data.T}{A #' SummarizedExperiment object of simulated tumor expression data. It is a #' \eqn{G} by \eqn{My} matrix, where \eqn{G} is the number of genes and #' \eqn{My} is the number of mixed samples.This is enabled only when #' output.more.info = TRUE.} \item{True.data.N1}{A SummarizedExperiment object #' of simulated true expression data for reference component 1 (e.g., stroma). #' It is a \eqn{G} by \eqn{M1} matrix where \eqn{G} is the number of genes and #' \eqn{M1} is the number of samples for component 1. This is enabled only when #' output.more.info = TRUE.} \item{True.data.N2}{A SummarizedExperiment object #' of simulated true expression data for reference component 2 (e.g., immue). #' It is a \eqn{G} by \eqn{M2} matrix where \eqn{G} is the number of genes and #' \eqn{M2} is the number of samples for component 2. This is enabled only when #' output.more.info = TRUE.} #' @keywords simulate_3comp #' @examples #' #' test.data = simulate_3comp(G1 = 675, G2 = 25, My = 20, M1 = 100, M2 = 100) #' test.data$pi #' test.data$Mu #' test.data$Sigma #' #' @export simulate_3comp simulate_3comp <- function(G1 = 675, G2 = 25, My = 20, M1 = 100, M2 = 100, output.more.info = FALSE){ requireNamespace("truncdist", quietly=TRUE) requireNamespace("SummarizedExperiment", quietly=TRUE) G = G1 + G2 ## Simulate MuN1, MuN2 and MuT for each gene MuN1 <- rnorm(G1 + G2, 7, 1.5) MuN2_1st <- MuN1[1:G1] + truncdist::rtrunc(n = 1, spec = 'norm', mean = 0, sd = 1.5, a = -0.1, b = 0.1) MuN2_2nd <- c() for(l in (G1+1):G){ tmp <- MuN1[l] + truncdist::rtrunc(n = 1, spec = 'norm', mean = 0, sd = 1.5, a = 0.1, b = 3)^rbinom(1, size=1, prob=0.5) while(tmp <= 0) tmp <- MuN1[l] + truncdist::rtrunc(n = 1, spec = 'norm', mean = 0, sd = 1.5, a = 0.1, b = 3)^rbinom(1, size=1, prob=0.5) MuN2_2nd <- c(MuN2_2nd, tmp) } MuN2 <- c(MuN2_1st, MuN2_2nd) MuT <- rnorm(G, 7, 1.5) Mu <- cbind(MuN1, MuN2, MuT) colnames(Mu) <- c('MuN1', 'MuN2', 'MuT') rownames(Mu) <- paste('Gene', seq = seq(1, G)) ## Simulate SigmaN1, SigmaN2 and SigmaT for each gene SigmaN1 <- runif(n = G, min = 0.1, max = 0.8) SigmaN2 <- runif(n = G, min = 0.1, max = 0.8) SigmaT <- runif(n = G, min = 0.1, max = 0.8) Sigma <- cbind(SigmaN1, SigmaN2, SigmaT) colnames(Sigma) <- c('SigmaN1', 'SigmaN2', 'SigmaT') rownames(Sigma) <- paste('Gene', seq = seq(1, G)) ## Initial values data.N1 <- matrix(0, G, M1) data.N2 <- matrix(0, G, M2) data.Y <- matrix(0, G, My) True.data.N1 <- matrix(0, G, My) True.data.N2 <- matrix(0, G, My) True.data.T <- matrix(0, G, My) ## Creat row and column name rownames(data.N1) <- rownames(data.N2) <- rownames(data.Y) <- rownames(True.data.N1) <- rownames(True.data.N2) <- rownames(True.data.T) <- paste('Gene', seq = seq(1, G)) colnames(data.N1) <- paste('Sample', seq = seq(1, M1)) colnames(data.N2) <- paste('Sample', seq = seq(1, M2)) colnames(data.Y) <- colnames(True.data.N1) <- colnames(True.data.N2) <- colnames(True.data.T) <- paste('Sample', seq = seq(1, My)) ## Simulate Tumor Proportion pi <- matrix(0, 3, My) pi[1,] <- runif(n = My, min = 0.01, max = 0.97) for(j in 1:My){ pi[2, j] <- runif(n = 1, min = 0.01, max = 0.98 - pi[1,j]) pi[3, j] <- 1 - sum(pi[,j]) } rownames(pi) <- c('PiN1', 'PiN2', 'PiT') colnames(pi) <- paste('Sample', seq = seq(1, My)) ## Simulate Data for(k in 1:G){ data.N1[k,] <- 2^rnorm(M1, MuN1[k], SigmaN1[k]); # normal reference 1 data.N2[k,] <- 2^rnorm(M2, MuN2[k], SigmaN2[k]); # normal reference 1 True.data.T[k,] <- 2^rnorm(My, MuT[k], SigmaT[k]); # True Tumor True.data.N1[k,] <- 2^rnorm(My, MuN1[k], SigmaN1[k]); # True Normal 1 True.data.N2[k,] <- 2^rnorm(My, MuN2[k], SigmaN2[k]); # True Normal 1 data.Y[k,] <- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.N2[k,] + pi[3,]*True.data.T[k,] # Mixture Tumor } ## Transfer into bioconductor format data.Y <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(data.Y)), rowData = DataFrame(row.names = rownames(data.Y)), colData = DataFrame(row.names = colnames(data.Y))) data.N1 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(data.N1)), rowData = DataFrame(row.names = rownames(data.N1)), colData = DataFrame(row.names = colnames(data.N1))) data.N2 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(data.N2)), rowData = DataFrame(row.names = rownames(data.N2)), colData = DataFrame(row.names = colnames(data.N2))) True.data.T <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(True.data.T)), rowData = DataFrame(row.names = rownames(True.data.T)), colData = DataFrame(row.names = colnames(True.data.T))) True.data.N1 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(True.data.N1)), rowData = DataFrame(row.names = rownames(True.data.N1)), colData = DataFrame(row.names = colnames(True.data.N1))) True.data.N2 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(True.data.N2)), rowData = DataFrame(row.names = rownames(True.data.N2)), colData = DataFrame(row.names = colnames(True.data.N2))) if(output.more.info){ test.data = list(pi=pi, Mu = Mu, Sigma = Sigma, data.Y = data.Y, data.N1 = data.N1, data.N2 = data.N2, True.data.N1 = True.data.N1, True.data.N2 = True.data.N2, True.data.T = True.data.T) }else{ test.data = list(pi=pi, Mu = Mu, Sigma = Sigma, data.Y = data.Y, data.N1 = data.N1, data.N2 = data.N2) } return(test.data) } DeMixT/R/test.data.2comp.R0000644000175200017520000000367314710233064016171 0ustar00biocbuildbiocbuild#' @title Simulated two-component test data #' #' @description A list of simulated two-component test data used in #' DeMixT function. Expression data with 500 genes and 100 samples #' are simulated. #' #' @return A list with 5 elements (2 more elements when output.more.info = #' TRUE), which are #' \item{pi}{A matrix of estimated proportion. First row and second row #' corresponds to the proportion estimate for the known components and unkown #' component respectively for two or three component settings. Each column #' corresponds to one sample.} #' \item{Mu}{Simulated \eqn{Mu} of log2-normal distribution for both known #' (\eqn{MuN1}) and unknown component (\eqn{MuT}).} #' \item{Sigma}{Simulated \eqn{Sigma} of log2-normal distribution for both #' known (\eqn{SigmaN1}) and unknown component (\eqn{SigmaT}).} #' \item{data.Y}{A SummarizedExperiment object of expression data from mixed #' tumor samples. It is a \eqn{G} by \eqn{My} matrix where \eqn{G} is the number #' of genes and \eqn{My} is the number of mixed samples. Samples with the same #' tissue type should be placed together in columns.} #' \item{data.N1}{A SummarizedExperiment object of expression data #' from reference component 1 (e.g., normal). It is a \eqn{G} by \eqn{M1} matrix #' where \eqn{G} is the number of genes and \eqn{M1} is the number of samples #' for component 1.} #' \item{True.data.T}{A SummarizedExperiment object of simulated tumor expression #' data. It is a \eqn{G} by \eqn{My} matrix, where \eqn{G} is the number of #' genes and \eqn{My} is the number of mixed samples. This is shown only when #' output.more.info = TRUE.} #' \item{True.data.N1}{A SummarizedExperiment object of simulated true #' expression data for reference component 1 (e.g., normal). It is a \eqn{G} #' by \eqn{M1} matrix where \eqn{G} is the number of genes and \eqn{M1} is the #' number of samples for component 1. This is shown only when #' output.more.info = TRUE.} 'test.data.2comp'DeMixT/R/test.data.3comp.R0000644000175200017520000000515414710233064016166 0ustar00biocbuildbiocbuild#' @title Simulated three-component mixed cell line test data #' #' @description A list of simulated three-component mixed cell line #' test data used in DeMixT function. Expression data with 700 genes #' and 20 samples are simulated, where 675 genes' \eqn{MuN1} is close #' to \eqn{MuN2}. #' #' @return A list with 6 elements (3 more elements when output.more.info = #' TRUE), which are #' \item{pi}{A matrix of estimated proportion. First row and second row #' corresponds to the proportion estimate for the known components and unkown #' component respectively for two or three component settings. Each column #' corresponds to one sample.} #' \item{Mu}{Simulated \eqn{Mu} of log2-normal distribution for both known #' (\eqn{MuN1, MuN2}) and unknown component (\eqn{MuT}).} #' \item{Sigma}{Simulated \eqn{Sigma} of log2-normal distribution for both #' known (\eqn{SigmaN1, SigmaN2}) and unknown component #' (\eqn{SigmaT}).} #' \item{data.Y}{A SummarizedExperiment object of simulated expression data #' from mixed tumor samples. It is a \eqn{G} by \eqn{My} matrix where \eqn{G} #' is the number of genes and \eqn{My} is the number of mixed samples. #' Samples with the same tissue type should be placed together in columns.} #' \item{data.N1}{A SummarizedExperiment object of simulated expression data #' from reference component 1 (e.g., normal). It is a \eqn{G} by \eqn{M1} matrix #' where \eqn{G} is the number of genes and \eqn{M1} is the number of samples #' for component 1.} #' \item{data.N2}{A SummarizedExperiment object of expression data from #' additional reference samples. It is a \eqn{G} by \eqn{M2} matrix where #' \eqn{G} is the number of genes and \eqn{M2} is the number of samples for #' component 2.} #' \item{True.data.T}{A SummarizedExperiment object of simulated tumor expression #' data. It is a \eqn{G} by \eqn{My} matrix, where \eqn{G} is the number of #' genes and \eqn{My} is the number of mixed samples.This is shown only when #' output.more.info = TRUE.} #' \item{True.data.N1}{A SummarizedExperiment object of simulated true #' expression data for reference component 1 (e.g., stroma). It is a \eqn{G} #' by \eqn{M1} matrix where \eqn{G} is the number of genes and \eqn{M1} is the #' number of samples for component 1. This is shown only when #' output.more.info = TRUE.} #' \item{True.data.N2}{A SummarizedExperiment object of simulated true #' expression data for reference component 2 (e.g., immue). It is a \eqn{G} #' by \eqn{M2} matrix where \eqn{G} is the number of genes and \eqn{M2} is the #' number of samples for component 2. This is shown only when #' output.more.info = TRUE.} 'test.data.3comp'DeMixT/R/zzz.R0000644000175200017520000000027714710233064014115 0ustar00biocbuildbiocbuild#zzz.R .onAttach <- function(...) { result <- .C("checkopenmp", numthread=as.integer(0)) packageStartupMessage("OpenMP installed.\nNumber of threads = ", result$numthread) }DeMixT/README.md0000644000175200017520000000510214710233064014203 0ustar00biocbuildbiocbuild# DeMixT (v 1.20.1) DeMixT is a frequentist-based method and fast in yielding accurate estimates of cell proportions and compartment-specific expression profiles for two- and three-component deconvolution from heterogeneous tumor samples. # Updates (06/23/2024) Script DeMixT_preprocessing.R now inside the R package for input data preprocessing, to be consistent with the bioconductor version 1.20. The DeMixT tutorial at https://wwylab.github.io/DeMixT/ is updated. (06/18/2022) Fixed a bug in ``R/DeMixT_GS.R:130``: changed ``return(class(try(solve(m), silent = T)) == "matrix")`` to ``return(class(try(solve(m), silent = T))[1] == "matrix")`` to make it work properly under both ``R 3.x`` and ``R 4.x``; since under ``R 4.x``, ``class(matrix object)`` returns ``"matrix", "array"``, instead only ``"matrix"`` under ``R 3.x``. Please be aware of this bug for those who installed ``DeMixT``(v1.10.0) from Bioconductor. # Installation The DeMixT package is compatible with Windows, Linux and MacOS. Specifically, for Linux and MacOS, the user can install the latest ``DeMixT`` (v 1.20.1) from GitHub: ``` if (!require("devtools", quietly = TRUE)) install.packages('devtools') devtools::install_github("wwylab/DeMixT") ``` For Windows, we recommend the user to install DeMixT (v 1.20.0) from ``Bioconductor``: ``` if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("DeMixT") ``` Please note, Linux and MacOS users can also install DeMixT from ``Bioconductor``. Check if ``DeMixT`` is installed successfully: ``` # load package library(DeMixT) ``` **We highly recommend the user to use DeMixT on Linux or Windows machines.** # Issues of DeMixT on MacOS 1. DeMixT relies on OpenMP for parallel computing. Starting from R 4.00, R no longer supports OpenMP on MacOS, meaning the user can only run DeMixT with one core on MacOS. 2. We noticed there may be installation/running errors for DeMixT on MacOS machines with M1 chips. We are tring to fix it. # Use ``DeMixT`` A tutorial is available at [https://wwylab.github.io/DeMixT/](https://wwylab.github.io/DeMixT/). # Cite ``DeMixT`` [1] Ahn, J. et al. DeMix: Deconvolution for mixed cancer transcriptomes using raw measured data. Bioinformatics 29, 1865–1871 (2013). [2] Wang, Z. et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience 9, 451–460 (2018). [3] Cao, S. et al. Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression. Nature Biotechnology Published online June 13 2022. doi 10.1038/s41587-022-01342-x. DeMixT/build/0000755000175200017520000000000014710276060014030 5ustar00biocbuildbiocbuildDeMixT/build/vignette.rds0000644000175200017520000000030514710276060016365 0ustar00biocbuildbiocbuildb```b`aad`b2 1# 'JIͬ( MAqIͬ" ՓQ&3h0X",LHXsS4楀a>"5lP5,n90{C2K7(1 棸(\^P87@btr$$ VDeMixT/data/0000755000175200017520000000000014710233064013637 5ustar00biocbuildbiocbuildDeMixT/data-raw/0000755000175200017520000000000014710233064014426 5ustar00biocbuildbiocbuildDeMixT/data-raw/DATASET.R0000644000175200017520000002312414710233064015640 0ustar00biocbuildbiocbuild## code to prepare `DATASET` dataset goes here ##-------------------------------------------------------------------------- ## Simulate Data for two-component ##-------------------------------------------------------------------------- simulate_2comp <- function(G = 500, My = 100, M1 = 100, output.more.info = FALSE){ requireNamespace("truncdist", quietly=TRUE) requireNamespace("SummarizedExperiment", quietly=TRUE) ## Simulate MuN and MuT for each gene MuN <- rnorm(G, 7, 1.5) MuT <- rnorm(G, 7, 1.5) Mu <- cbind(MuN, MuT) colnames(Mu) <- c('MuN', 'MuT') rownames(Mu) <- paste('Gene', seq = seq(1, G)) ## Simulate SigmaN and SigmaT for each gene SigmaN <- runif(n = G, min = 0.1, max = 0.8) SigmaT <- runif(n = G, min = 0.1, max = 0.8) Sigma <- cbind(SigmaN, SigmaT) colnames(Sigma) <- c('SigmaN', 'SigmaT') rownames(Sigma) <- paste('Gene', seq = seq(1, G)) ## Initial values data.N1 <- matrix(0, G, M1) data.Y <- matrix(0, G, My) True.data.N1 <- matrix(0, G, My) True.data.T <- matrix(0, G, My) ## Creat row and column name rownames(data.N1) <- rownames(data.Y) <- rownames(True.data.N1) <- rownames(True.data.T) <- paste('Gene', seq = seq(1, G)) colnames(data.N1) <- paste('Sample', seq = seq(1, M1)) colnames(data.Y) <- colnames(True.data.N1) <- colnames(True.data.T) <- paste('Sample', seq = seq(1, My)) ## Simulate Tumor Proportion PiT = truncdist::rtrunc(n = My, spec = 'norm', mean = 0.55, sd = 0.2, a = 0.25, b = 0.95) pi <- rbind(1-PiT, PiT) rownames(pi) <- c('PiN', 'PiT') colnames(pi) <- paste('Sample', seq = seq(1, My)) ## Simulate Data for(k in 1:G){ data.N1[k,] <- 2^rnorm(M1, MuN[k], SigmaN[k]); # normal reference True.data.T[k,] <- 2^rnorm(My, MuT[k], SigmaT[k]); # True Tumor True.data.N1[k,] <- 2^rnorm(My, MuN[k], SigmaN[k]); # True Normal data.Y[k,] <- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.T[k,] # Mixture Tumor } ## Transfer into bioconductor format data.Y <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(data.Y)), rowData = DataFrame(row.names = rownames(data.Y)), colData = DataFrame(row.names = colnames(data.Y))) data.N1 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(data.N1)), rowData = DataFrame(row.names = rownames(data.N1)), colData = DataFrame(row.names = colnames(data.N1))) True.data.T <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(True.data.T)), rowData = DataFrame(row.names = rownames(True.data.T)), colData = DataFrame(row.names = colnames(True.data.T))) True.data.N1 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(True.data.N1)), rowData = DataFrame(row.names = rownames(True.data.N1)), colData = DataFrame(row.names = colnames(True.data.N1))) if(output.more.info){ test.data = list(pi=pi, Mu = Mu, Sigma = Sigma, data.Y = data.Y, data.N1 = data.N1, True.data.T = True.data.T, True.data.N1 = True.data.N1) }else{ test.data = list(pi=pi, Mu = Mu, Sigma = Sigma, data.Y = data.Y, data.N1 = data.N1) } return(test.data) } set.seed(123) test.data.2comp = simulate_2comp(G = 500, My = 100, M1 = 100) usethis::use_data(test.data.2comp, compress = 'xz', overwrite = TRUE) ##-------------------------------------------------------------------------- ## Simulate Data for three-component mixed cell line ##-------------------------------------------------------------------------- simulate_3comp <- function(G1 = 475, G2 = 25, My = 5, M1 = 100, M2 = 100, output.more.info = FALSE){ requireNamespace("truncdist", quietly=TRUE) requireNamespace("SummarizedExperiment", quietly=TRUE) G = G1 + G2 ## Simulate MuN1, MuN2 and MuT for each gene MuN1 <- rnorm(G1 + G2, 7, 1.5) MuN2_1st <- MuN1[1:G1] + truncdist::rtrunc(n = 1, spec = 'norm', mean = 0, sd = 1.5, a = -0.1, b = 0.1) MuN2_2nd <- c() for(l in (G1+1):G){ tmp <- MuN1[l] + truncdist::rtrunc(n = 1, spec = 'norm', mean = 0, sd = 1.5, a = 0.1, b = 3)^rbinom(1, size=1, prob=0.5) while(tmp <= 0) tmp <- MuN1[l] + truncdist::rtrunc(n = 1, spec = 'norm', mean = 0, sd = 1.5, a = 0.1, b = 3)^rbinom(1, size=1, prob=0.5) MuN2_2nd <- c(MuN2_2nd, tmp) } MuN2 <- c(MuN2_1st, MuN2_2nd) MuT <- rnorm(G, 7, 1.5) Mu <- cbind(MuN1, MuN2, MuT) colnames(Mu) <- c('MuN1', 'MuN2', 'MuT') rownames(Mu) <- paste('Gene', seq = seq(1, G)) ## Simulate SigmaN1, SigmaN2 and SigmaT for each gene SigmaN1 <- runif(n = G, min = 0.1, max = 0.8) SigmaN2 <- runif(n = G, min = 0.1, max = 0.8) SigmaT <- runif(n = G, min = 0.1, max = 0.8) Sigma <- cbind(SigmaN1, SigmaN2, SigmaT) colnames(Sigma) <- c('SigmaN1', 'SigmaN2', 'SigmaT') rownames(Sigma) <- paste('Gene', seq = seq(1, G)) ## Initial values data.N1 <- matrix(0, G, M1) data.N2 <- matrix(0, G, M2) data.Y <- matrix(0, G, My) True.data.N1 <- matrix(0, G, My) True.data.N2 <- matrix(0, G, My) True.data.T <- matrix(0, G, My) ## Creat row and column name rownames(data.N1) <- rownames(data.N2) <- rownames(data.Y) <- rownames(True.data.N1) <- rownames(True.data.N2) <- rownames(True.data.T) <- paste('Gene', seq = seq(1, G)) colnames(data.N1) <- paste('Sample', seq = seq(1, M1)) colnames(data.N2) <- paste('Sample', seq = seq(1, M2)) colnames(data.Y) <- colnames(True.data.N1) <- colnames(True.data.N2) <- colnames(True.data.T) <- paste('Sample', seq = seq(1, My)) ## Simulate Tumor Proportion pi <- matrix(0, 3, My) pi[1,] <- runif(n = My, min = 0.01, max = 0.97) for(j in 1:My){ pi[2, j] <- runif(n = 1, min = 0.01, max = 0.98 - pi[1,j]) pi[3, j] <- 1 - sum(pi[,j]) } rownames(pi) <- c('PiN1', 'PiN2', 'PiT') colnames(pi) <- paste('Sample', seq = seq(1, My)) ## Simulate Data for(k in 1:G){ data.N1[k,] <- 2^rnorm(M1, MuN1[k], SigmaN1[k]); # normal reference 1 data.N2[k,] <- 2^rnorm(M2, MuN2[k], SigmaN2[k]); # normal reference 1 True.data.T[k,] <- 2^rnorm(My, MuT[k], SigmaT[k]); # True Tumor True.data.N1[k,] <- 2^rnorm(My, MuN1[k], SigmaN1[k]); # True Normal 1 True.data.N2[k,] <- 2^rnorm(My, MuN2[k], SigmaN2[k]); # True Normal 1 data.Y[k,] <- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.N2[k,] + pi[3,]*True.data.T[k,] # Mixture Tumor } ## Transfer into bioconductor format data.Y <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(data.Y)), rowData = DataFrame(row.names = rownames(data.Y)), colData = DataFrame(row.names = colnames(data.Y))) data.N1 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(data.N1)), rowData = DataFrame(row.names = rownames(data.N1)), colData = DataFrame(row.names = colnames(data.N1))) data.N2 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(data.N2)), rowData = DataFrame(row.names = rownames(data.N2)), colData = DataFrame(row.names = colnames(data.N2))) True.data.T <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(True.data.T)), rowData = DataFrame(row.names = rownames(True.data.T)), colData = DataFrame(row.names = colnames(True.data.T))) True.data.N1 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(True.data.N1)), rowData = DataFrame(row.names = rownames(True.data.N1)), colData = DataFrame(row.names = colnames(True.data.N1))) True.data.N2 <- SummarizedExperiment(assays = SimpleList(counts = as.matrix(True.data.N2)), rowData = DataFrame(row.names = rownames(True.data.N2)), colData = DataFrame(row.names = colnames(True.data.N2))) if(output.more.info){ test.data = list(pi=pi, Mu = Mu, Sigma = Sigma, data.Y = data.Y, data.N1 = data.N1, data.N2 = data.N2, True.data.N1 = True.data.N1, True.data.N2 = True.data.N2, True.data.T = True.data.T) }else{ test.data = list(pi=pi, Mu = Mu, Sigma = 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C7P(bV-nӂ%B% Af]}/'=r_1sHV(ZpxE@82Ơ ԋn߼%yGh?\Z;:{TΉ64'eYD8MnpޓMgK7䳳ocs,Hʗkf%?㍨O\ANyCټZ˱/1up( Q)$\bpo7MP.1g'Fu\Ѣ&K6͏V|ؠAB)"*~ !{a7'c9!QNl-#ɟbvJف}Q_ /owƒ^cLds]u-EHY) RkjJ.rT]p3l_W7CaO +-i2`<3; U"gA@n\XTiRb/z)x4!QjZ'E )HɆPgq5D {YXSCXps=cKfE̠0A0h[[&^n>A t"A|b̑YX;A>ęŦ=CM9.ulw5MQT@Nc锤>b-J(r =cfԕՕHSS<>0 YZDeMixT/inst/0000755000175200017520000000000014710233064013703 5ustar00biocbuildbiocbuildDeMixT/inst/CITATION0000644000175200017520000000117014710233064015037 0ustar00biocbuildbiocbuildcitHeader("To cite package DeMixT in publications use:") citEntry(entry = "Article", title = "Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration.", author = personList(as.person("Zeya Wang,"), as.person("et al")), journal = "iScience", year = "2018", url = "https://doi.org/10.1016/j.isci.2018.10.028", textVersion = paste("Zeya Wang, et al (2018).", "Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration.", "iScience.", "doi:https://doi.org/10.1016/j.isci.2018.10.028") ) DeMixT/inst/NEWS.Rd0000644000175200017520000000347614710233064014760 0ustar00biocbuildbiocbuild\name{NEWS} \title{News for Package \pkg{DeMixT}} \section{Version 1.6.0}{ \itemize{ \item MPI for parallel computing is avaliable under R 4.0.0 for linux and Mac OS platforms. \item Gene expression data of normal tissues (Lung, Prostate and Thyroid) from the GTEx study are included. \item Rename DeMixT_S1 function to DeMixT_DE. }} \section{Version 1.4.0}{ \itemize{ \item Disabled OpenMP under R 4.0.0 for Mac OS platform. }} \section{Version 1.2.5}{ \itemize{ \item Added pi01 and pi02 as input values for users to initialize the proportion estimation. \item Added nspikein as an input value in the DeMixT, DeMixT_S1 and DeMixT_GS functions to specify how many spike-in normal reference samples need to be generated; Setting nspikein at null as a default value, the number of spike-in normal reference samples equal the min(200, 0.3 x My), where My is the number of mixed samples; By setting nspikein equals 0, no spike-in normal reference will be generated; If the input value of data.N2 is not null, nspikein will be forced to be 0. \item Added DeMixT_GS function, new proposed gene selection method which applies profile likelihood, for proportion estimation. \item Added simulate_2comp function for users to simulate test data for 2-component de-convolution. \item Added simulate_3comp function for users to simulate test data for 3-component de-convolution. \item Added row names and column names for all output values. \item Added gene.selection.method as an input value for DeMixT function. The default is 'GS'. \item Added ngene.Profile.selected as an input value for DeMixT function. The default is NA.} } \section{Version 0.99.0}{ \itemize{ \item New package DeMixT, for cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms }} DeMixT/inst/doc/0000755000175200017520000000000014710233064014450 5ustar00biocbuildbiocbuildDeMixT/inst/doc/demixt.R0000644000175200017520000001771314710276057016107 0ustar00biocbuildbiocbuild## ----setup, include=FALSE, message=FALSE-------------------------------------- library(ggplot2) library(DeMixT) plot.PCA =function(indata, batch, figure.dir, PCA.fig.title, legend.position = 'bottomleft', label = FALSE, xlimit = NULL, ofile=TRUE, lines = TRUE) { # data is a data matrix with samples in columns and genes in rows. # batch is a vector with the order matching the order in indata. #batch=as.numeric(batch) N.batch = length(unique(batch)) if(file.exists(paste0('Batch_Effect/', PCA.fig.title, '.RData'))){ load(paste0('Batch_Effect/', PCA.fig.title, '.RData')) }else{ pca <- SamplePCA(indata, usecor=F, center=T) save(pca, file = paste0('Batch_Effect/', PCA.fig.title, '.RData')) } pct1 <- round (pca@variances[1]/sum(pca@variances), digits=3)*100 pct2 <- round (pca@variances[2]/sum(pca@variances), digits=3)*100 xlab.text = paste("First Comp: ", as.character(pct1), "% variance", sep="") ylab.text = paste("Second Comp: ", as.character(pct2), "% variance", sep="") #jpeg(file=file.path(figure.dir, paste("PCA_", PCA.fig.title, ".jpeg", sep="")), width = 600, height = 600, quality=100, pointsize=16) if(ofile) pdf(file=file.path(figure.dir, paste("PCA_", PCA.fig.title, ".pdf", sep=""))) plot(pca@scores[,1], pca@scores[,2], cex=1, xlab=xlab.text, ylab=ylab.text, col=rainbow(N.batch)[as.numeric(factor(batch))], pch=as.numeric(factor((batch))),lwd=1.5, main=PCA.fig.title) if(label == TRUE) { library(calibrate) textxy(pca@scores[,1], pca@scores[,2],colnames(indata), cex=0.75) } abline(h=0, v=0, col="brown", lty=2) abline(h=0, v=0, col="brown", lty=2) center1<-tapply(pca@scores[,1], factor(batch), mean) center2<-tapply(pca@scores[,2], factor(batch), mean) if(lines){ for (ii in 1:length(center1)) { groupi<-pca@scores[as.numeric(factor(batch))==ii, 1:2] if (class(groupi)=="matrix") { for (j in (1:nrow(groupi))) { segments( groupi[j,1], groupi[j,2], center1[ii], center2[ii], col=rainbow(N.batch)[ii] , lwd=0.3) } }else { segments( groupi[1], groupi[2], center1[ii], center2[ii], col=rainbow(N.batch)[ii] , lwd=0.3) } } } points(center1, center2, pch=7, lwd=1.5,col=rainbow(N.batch)) legend(legend.position,legend=names(table(factor(batch))), text.col=rainbow(N.batch), pch=1:8, col=rainbow(N.batch), lty=1) if(ofile) invisible(dev.off()) } ## ----Algorithm, echo=FALSE, out.width='100%'---------------------------------- knitr::include_graphics(path = paste0("Algorithm.png")) ## ----sim_2comp_GS, results="hide", message=FALSE------------------------------ data("test.data.2comp") # res.GS = DeMixT_GS(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 30, nbin = 50, nspikein = 50, # if.filter = TRUE, ngene.Profile.selected = 150, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) load('Res_2comp/res.GS.RData') ## ----sim_2comp_GS_res--------------------------------------------------------- head(t(res.GS$pi)) head(res.GS$gene.name) ## ----sim_2comp_S2, results="hide", message=FALSE------------------------------ data("test.data.2comp") # res.S2 <- DeMixT_S2(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # data.N2 = NULL, # givenpi = c(t(res.S1$pi[-nrow(res.GS$pi),])), nbin = 50) load('Res_2comp/res.S2.RData') ## ----sim_2comp_S2_res--------------------------------------------------------- head(res.S2$decovExprT[,1:5],3) head(res.S2$decovExprN1[,1:5],3) head(res.S2$decovMu,3) head(res.S2$decovSigma,3) ## ----read_data_2comp, warning=FALSE, message=FALSE--------------------------- # ## DeMixT_DE without Spike-in Normal # res.S1 = DeMixT_DE(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 30, nbin = 50, nspikein = 0, # if.filter = TRUE, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) # ## DeMixT_DE with Spike-in Normal # res.S1.SP = DeMixT_DE(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 30, nbin = 50, nspikein = 50, # if.filter = TRUE, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) # ## DeMixT_GS with Spike-in Normal # res.GS.SP = DeMixT_GS(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 30, nbin = 50, nspikein = 50, # if.filter = TRUE, ngene.Profile.selected = 150, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) load('Res_2comp/res.S1.RData'); load('Res_2comp/res.S1.SP.RData'); load('Res_2comp/res.GS.RData'); load('Res_2comp/res.GS.SP.RData'); ## ----sim_2comp, fig.height = 4, fig.width = 6, fig.align='center', warning=FALSE---- res.2comp = as.data.frame(cbind(round(rep(t(test.data.2comp$pi[2,]),3),2), round(c(t(res.S1$pi[2,]),t(res.S1.SP$pi[2,]), t(res.GS.SP$pi[2,])),2), rep(c('DE','DE-SP','GS-SP'), each = 100)), num = 1:2) res.2comp$V1 <- as.numeric(as.character(res.2comp$V1)) res.2comp$V2 <- as.numeric(as.character(res.2comp$V2)) res.2comp$V3 = as.factor(res.2comp$V3) names(res.2comp) = c('True.Proportion', 'Estimated.Proportion', 'Method') ## Plot ggplot(res.2comp, aes(x=True.Proportion, y=Estimated.Proportion, group = Method, color=Method, shape=Method)) + geom_point() + geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "black", lwd = 0.5) + xlim(0,1) + ylim(0,1) + scale_shape_manual(values=c(seq(1:3))) + labs(x = 'True Proportion', y = 'Estimated Proportion') ## ----read_data_3comp, warning=FALSE, message=FALSE--------------------------- data("test.data.3comp") # res.S1 <- DeMixT_DE(data.Y = test.data.3comp$data.Y, data.N1 = test.data.3comp$data.N1, # data.N2 = test.data.3comp$data.N2, if.filter = TRUE) load('Res_3comp/res.S1.RData'); ## ----sim_3comp, fig.height = 4, fig.width = 6, fig.align='center', warning=FALSE---- res.3comp= as.data.frame(cbind(round(t(matrix(t(test.data.3comp$pi), nrow = 1)),2), round(t(matrix(t(res.S1$pi), nrow = 1)),2), rep(c('N1','N2','T'), each = 20))) res.3comp$V1 <- as.numeric(as.character(res.3comp$V1)) res.3comp$V2 <- as.numeric(as.character(res.3comp$V2)) res.3comp$V3 = as.factor(res.3comp$V3) names(res.3comp) = c('True.Proportion', 'Estimated.Proportion', 'Component') ## Plot ggplot(res.3comp, aes(x=True.Proportion, y=Estimated.Proportion, group = Component, color=Component, shape=Component)) + geom_point() + geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "black", lwd = 0.5) + xlim(0,1) + ylim(0,1) + scale_shape_manual(values=c(seq(1:3))) + labs(x = 'True Proportion', y = 'Estimated Proportion') ## ----load_data, fig.height = 4, fig.width = 6, fig.align='center', warning=FALSE---- load(url("https://wwylab.github.io/DeMixT/etc/PRAD.RData")) ## ----view_data, fig.height = 4, fig.width = 6, fig.align='center', warning=FALSE---- head(PRAD[,1:5]) cat('Number of genes: ', dim(PRAD)[1], '\n') cat('Number of normal sample: ', length(Normal.id), '\n') cat('Number of tumor sample: ', length(Tumor.id), '\n') ## ----correlation, echo=FALSE, out.width='100%'-------------------------------- knitr::include_graphics(path = paste0("pairwise_correlation.png")) ## ----GTEx, echo=FALSE, out.width='65%', fig.align='center'-------------------- knitr::include_graphics(path = paste0("GTEx_normal.png")) ## ----------------------------------------------------------------------------- sessionInfo(package = "DeMixT") DeMixT/inst/doc/demixt.Rmd0000644000175200017520000007770514710233064016426 0ustar00biocbuildbiocbuild--- title: "A Vignette for DeMixT" date: "Last updated: `r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{DeMixT.Rmd} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} ---

```{r setup, include=FALSE, message=FALSE} library(ggplot2) library(DeMixT) plot.PCA =function(indata, batch, figure.dir, PCA.fig.title, legend.position = 'bottomleft', label = FALSE, xlimit = NULL, ofile=TRUE, lines = TRUE) { # data is a data matrix with samples in columns and genes in rows. # batch is a vector with the order matching the order in indata. #batch=as.numeric(batch) N.batch = length(unique(batch)) if(file.exists(paste0('Batch_Effect/', PCA.fig.title, '.RData'))){ load(paste0('Batch_Effect/', PCA.fig.title, '.RData')) }else{ pca <- SamplePCA(indata, usecor=F, center=T) save(pca, file = paste0('Batch_Effect/', PCA.fig.title, '.RData')) } pct1 <- round (pca@variances[1]/sum(pca@variances), digits=3)*100 pct2 <- round (pca@variances[2]/sum(pca@variances), digits=3)*100 xlab.text = paste("First Comp: ", as.character(pct1), "% variance", sep="") ylab.text = paste("Second Comp: ", as.character(pct2), "% variance", sep="") #jpeg(file=file.path(figure.dir, paste("PCA_", PCA.fig.title, ".jpeg", sep="")), width = 600, height = 600, quality=100, pointsize=16) if(ofile) pdf(file=file.path(figure.dir, paste("PCA_", PCA.fig.title, ".pdf", sep=""))) plot(pca@scores[,1], pca@scores[,2], cex=1, xlab=xlab.text, ylab=ylab.text, col=rainbow(N.batch)[as.numeric(factor(batch))], pch=as.numeric(factor((batch))),lwd=1.5, main=PCA.fig.title) if(label == TRUE) { library(calibrate) textxy(pca@scores[,1], pca@scores[,2],colnames(indata), cex=0.75) } abline(h=0, v=0, col="brown", lty=2) abline(h=0, v=0, col="brown", lty=2) center1<-tapply(pca@scores[,1], factor(batch), mean) center2<-tapply(pca@scores[,2], factor(batch), mean) if(lines){ for (ii in 1:length(center1)) { groupi<-pca@scores[as.numeric(factor(batch))==ii, 1:2] if (class(groupi)=="matrix") { for (j in (1:nrow(groupi))) { segments( groupi[j,1], groupi[j,2], center1[ii], center2[ii], col=rainbow(N.batch)[ii] , lwd=0.3) } }else { segments( groupi[1], groupi[2], center1[ii], center2[ii], col=rainbow(N.batch)[ii] , lwd=0.3) } } } points(center1, center2, pch=7, lwd=1.5,col=rainbow(N.batch)) legend(legend.position,legend=names(table(factor(batch))), text.col=rainbow(N.batch), pch=1:8, col=rainbow(N.batch), lty=1) if(ofile) invisible(dev.off()) } ``` # 1. Introduction Transcriptomic deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We develop a three-component deconvolution model, DeMixT, for expression data from a mixture of cancerous tissues, infiltrating immune cells and tumor microenvironment. DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components. DeMixT is a frequentist-based method and fast in yielding accurate estimates of cell proportions and compart-ment-specific expression profiles for two-component \and three-component deconvolution problem. Our method promises to provide deeper insight into cancer biomarkers and assist in the development of novel prognostic markers and therapeutic strategies. The function DeMixT is designed to finish the whole pipeline of deconvolution for two or three components. The newly added DeMixT_GS function is designed to estimates the proportions of mixed samples for each mixing component based on a new approach to select genes more effectively that utilizes profile likelihood. DeMixT_DE function is designed to estimate the proportions of all mixed samples for each mixing component based on the gene differential expressions to select genes. DeMixT_S2 function is designed to estimate the component-specific deconvolved expressions of individual mixed samples for a given set of genes. # 2 Feature Description The DeMixT R-package builds the transcriptomic deconvolution with a couple of novel features into R-based standard analysis pipeline through Bioconductor. DeMixT showed high accuracy and efficiency from our designed experiment. Hence, DeMixT can be considered as an important step towards linking tumor transcriptomic data with clinical outcomes. Different from most previous computational deconvolution methods, DeMixT has integrated new features for the deconvolution with more than 2 components. **Joint estimation**: jointly estimate component proportions and expression profiles for individual samples by requiring reference samples instead of reference genes; For the three-component deconvolution considering immune infiltration, it provides a comprehensive view of tumor-stroma-immune transcriptional dynamics, as compared to methods that address only immune subtypes within the immune component, in each tumor sample. **Efficient estimation**: DeMixT adopts an approach of iterated conditional modes (ICM) to guarantee a rapid convergence to a local maximum. We also design a novel gene-set-based component merging approach to reduce the bias of proportion estimation for three-component deconvolutionthe. **Parallel computing**: OpenMP enable parallel computing on single computer by taking advantage of the multiple cores shipped on modern CPUs. The ICM framework further enables parallel computing, which helps compensate for the expensive computing time used in the repeated numerical double integrations. # 3. Installation The DeMixT package is compatible with Windows, Linux and MacOS. The user can install it from ``Bioconductor``: ``` if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("DeMixT") ``` For Linux and MacOS, the user can also install the latest DeMixT from GitHub: ``` if (!require("devtools", quietly = TRUE)) install.packages('devtools') devtools::install_github("wwylab/DeMixT") ``` Check if DeMixT is installed successfully: ``` # load package library(DeMixT) ``` **Note**: DeMixT relies on OpenMP for parallel computing. Starting from R 4.00, R no longer supports OpenMP on MacOS, meaning the user can only run DeMixT with one core on MacOS. We therefore recommend the users to mainly use Linux system for running DeMixT to take advantage of the multi-core parallel computation. # 4. Functions The following table shows the functions included in DeMixT. Table Header | Second Header ------- | ---------------------------------- DeMixT | Deconvolution of tumor samples with two or three components. DeMixT_GS | Estimates the proportions of mixed samples for each mixing component based on a new approach to select genes that utilizes profile likelihood. DeMixT_DE | Estimates the proportions of mixed samples for each mixing component. DeMixT_S2 |Deconvolves expressions of each sample for unknown component. Optimum_KernelC | Call the C function used for parameter estimation in DeMixT. DeMixT_Preprocessing | Preprocessing functions before running DeMixT. # 5. Methods ## 5.1 Model Let \(Y_{ig}\) be the observed expression levels of the raw measured data from clinically derived malignant tumor samples for gene \(g, g = 1, \cdots, G\) and sample \(i, i = 1, \cdots, My\). \(G\) denotes the total number of probes/genes and \(My\) denotes the number of samples. The observed expression levels for solid tumors can be modeled as a linear combination of raw expression levels from three components: \[ {Y_{ig}} = \pi _{1,i}N_{1,ig} + \pi _{2,i}N_{2,ig} + (1 - \pi_{1,i} - \pi _{2,i}){T_{ig}} \label{eq:1} \] Here \(N_{1,ig}\), \(N_{2,ig}\) and \({T_{ig}}\) are the unobserved raw expression levels from each of the three components. We call the two components for which we require reference samples the \(N_1\)-component and the \(N_2\)-component. We call the unknown component the T-component. We let \(\pi_{1,i}\) denote the proportion of the \(N_1\)-component, \(\pi_{2,i}\) denote the proportion of the \(N_2\)-component, and \(1 - \pi_{1,i}-\pi_{2,i}\) denote the proportion of the T-component. We assume that the mixing proportions of one specific sample remain the same across all genes. Our model allows for one component to be unknown, and therefore does not require reference profiles from all components. A set of samples for \(N_{1,ig}\) and \(N_{2,ig}\), respectively, needs to be provided as input data. This three-component deconvolution model is applicable to the linear combination of any three components in any type of material. It can also be simplified to a two-component model, assuming there is just one \(N\)-component. For application in this paper, we consider tumor (\(T\)), stromal (\(N_1\)) and immune components (\(N_2\)) in an admixed sample (\(Y\)). Following the convention that \(\log_2\)-transformed microarray gene expression data follow a normal distribution, we assume that the raw measures \(N_{1,ig} \sim LN({\mu _{{N_1}g}},\sigma _{{N_1}g}^2)\), \(N_{2,ig} \sim LN({\mu _{{N_2}g}},\sigma _{{N_2}g}^2)\) and \({T_{ig}} \sim LN({\mu _{Tg}}, \sigma _{Tg}^2)\), where LN denotes a \(\log_2\)-normal distribution and \(\sigma _{{N_1}g}^2\),\(\sigma _{{N_2}g}^2\), \(\sigma _{Tg}^2\) reflect the variations under \(\log_2\)-transformed data. Consequently, our model can be expressed as the convolution of the density function for three \(\log_2\)-normal distributions. Because there is no closed form of this convolution, we use numerical integration to evaluate the complete likelihood function (see the full likelihood in the Supplementary Materials in [1]). ## 5.2 The DeMixT algorithm for deconvolution DeMixT estimates all distribution parameters and cellular proportions and reconstitutes the expression profiles for all three components for each gene and each sample. The estimation procedure (summarized in Figure 1b) has two main steps as follows. 1. Obtain a set of parameters \(\{\pi_{1,i}, \pi_{2,i}\}_{i=1}^{My}\), \(\{\mu_T, \sigma_T\}_{g=1}^G\) to maximize the complete likelihood function, for which \(\{\mu_{N_{1,g}}, \sigma_{N_{1,g}}, \mu_{N_{2,g}}, \sigma_{N_{2,g}}\}_{g=1}^G\) were already estimated from the available unmatched samples of the \(N_1\) and \(N_2\) component tissues. (See further details in our paper.) 2. Reconstitute the expression profiles by searching each set of \(\{n_{1,ig}, n_{2,ig}\}\) that maximizes the joint density of \(N_{1,ig}\), \(N_{2,ig}\) and \(T_{ig}\). The value of \(t_{ig}\) is solved as \({y_{ig}} - {{\hat \pi }_{1,i}}{n_{1,ig}} - {{\hat \pi }_{2,i}}{n_{2,ig}}\). These two steps can be separately implemented using the function DeMixT_DE or DeMixT_GS for the first step and DeMixT_S2 for the second, which are combined in the function DeMixT(Note: DeMixT_GS is the default function for first step). Since version 1.8.2, DeMixT added simulated normal reference samples, i.e., spike-in, based on the observed normal reference samples. It has been shown to improve accuracy in proportion estimation for the scenario where a dataset consists of samples where true tumor proportions are skewed to the high end. ```{r Algorithm, echo=FALSE, out.width='100%'} knitr::include_graphics(path = paste0("Algorithm.png")) ``` # 6. Examples ## 6.1 Simulated two-component data ```{r, sim_2comp_GS, results="hide", message=FALSE} data("test.data.2comp") # res.GS = DeMixT_GS(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 30, nbin = 50, nspikein = 50, # if.filter = TRUE, ngene.Profile.selected = 150, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) load('Res_2comp/res.GS.RData') ``` ```{r sim_2comp_GS_res} head(t(res.GS$pi)) head(res.GS$gene.name) ``` ```{r, sim_2comp_S2, results="hide", message=FALSE} data("test.data.2comp") # res.S2 <- DeMixT_S2(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # data.N2 = NULL, # givenpi = c(t(res.S1$pi[-nrow(res.GS$pi),])), nbin = 50) load('Res_2comp/res.S2.RData') ``` ```{r sim_2comp_S2_res} head(res.S2$decovExprT[,1:5],3) head(res.S2$decovExprN1[,1:5],3) head(res.S2$decovMu,3) head(res.S2$decovSigma,3) ``` ## 6.2 Simulated two-component data In the simulation, ````markdown ## Simulate MuN and MuT for each gene MuN <- rnorm(G, 7, 1.5) MuT <- rnorm(G, 7, 1.5) Mu <- cbind(MuN, MuT) ## Simulate SigmaN and SigmaT for each gene SigmaN <- runif(n = G, min = 0.1, max = 0.8) SigmaT <- runif(n = G, min = 0.1, max = 0.8) ## Simulate Tumor Proportion PiT = truncdist::rtrunc(n = My, spec = 'norm', mean = 0.55, sd = 0.2, a = 0.25, b = 0.95) ## Simulate Data for(k in 1:G){ data.N1[k,] <- 2^rnorm(M1, MuN[k], SigmaN[k]); # normal reference True.data.T[k,] <- 2^rnorm(My, MuT[k], SigmaT[k]); # True Tumor True.data.N1[k,] <- 2^rnorm(My, MuN[k], SigmaN[k]); # True Normal data.Y[k,] <- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.T[k,] # Mixture Tumor } ```` where $\pi_i \in (0.25, 0.95)$ is from truncated normal distribution. In general, the true distribution of tumor proportion does not follow a uniform distribution between $[0,1]$, but instead skewed to the upper part of the interval. ```{r read_data_2comp, warning=FALSE, message=FALSE} # ## DeMixT_DE without Spike-in Normal # res.S1 = DeMixT_DE(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 30, nbin = 50, nspikein = 0, # if.filter = TRUE, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) # ## DeMixT_DE with Spike-in Normal # res.S1.SP = DeMixT_DE(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 30, nbin = 50, nspikein = 50, # if.filter = TRUE, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) # ## DeMixT_GS with Spike-in Normal # res.GS.SP = DeMixT_GS(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 30, nbin = 50, nspikein = 50, # if.filter = TRUE, ngene.Profile.selected = 150, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) load('Res_2comp/res.S1.RData'); load('Res_2comp/res.S1.SP.RData'); load('Res_2comp/res.GS.RData'); load('Res_2comp/res.GS.SP.RData'); ``` This simulation was designed to compare previous DeMixT resutls with DeMixT spike-in results under both gene selection method. ```{r sim_2comp, fig.height = 4, fig.width = 6, fig.align='center', warning=FALSE} res.2comp = as.data.frame(cbind(round(rep(t(test.data.2comp$pi[2,]),3),2), round(c(t(res.S1$pi[2,]),t(res.S1.SP$pi[2,]), t(res.GS.SP$pi[2,])),2), rep(c('DE','DE-SP','GS-SP'), each = 100)), num = 1:2) res.2comp$V1 <- as.numeric(as.character(res.2comp$V1)) res.2comp$V2 <- as.numeric(as.character(res.2comp$V2)) res.2comp$V3 = as.factor(res.2comp$V3) names(res.2comp) = c('True.Proportion', 'Estimated.Proportion', 'Method') ## Plot ggplot(res.2comp, aes(x=True.Proportion, y=Estimated.Proportion, group = Method, color=Method, shape=Method)) + geom_point() + geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "black", lwd = 0.5) + xlim(0,1) + ylim(0,1) + scale_shape_manual(values=c(seq(1:3))) + labs(x = 'True Proportion', y = 'Estimated Proportion') ``` ## 6.3 Simulated three-component data In this simulation, ````markdown G <- G1 + G2 ## Simulate MuN1, MuN2 and MuT for each gene MuN1 <- rnorm(G, 7, 1.5) MuN2_1st <- MuN1[1:G1] + truncdist::rtrunc(n = 1, spec = 'norm', mean = 0, sd = 1.5, a = -0.1, b = 0.1) MuN2_2nd <- c() for(l in (G1+1):G){ tmp <- MuN1[l] + truncdist::rtrunc(n = 1, spec = 'norm', mean = 0, sd = 1.5, a = 0.1, b = 3)^rbinom(1, size=1, prob=0.5) while(tmp <= 0) tmp <- MuN1[l] + truncdist::rtrunc(n = 1, spec = 'norm', mean = 0, sd = 1.5, a = 0.1, b = 3)^rbinom(1, size=1, prob=0.5) MuN2_2nd <- c(MuN2_2nd, tmp) } ## Simulate SigmaN1, SigmaN2 and SigmaT for each gene SigmaN1 <- runif(n = G, min = 0.1, max = 0.8) SigmaN2 <- runif(n = G, min = 0.1, max = 0.8) SigmaT <- runif(n = G, min = 0.1, max = 0.8) ## Simulate Tumor Proportion pi <- matrix(0, 3, My) pi[1,] <- runif(n = My, min = 0.01, max = 0.97) for(j in 1:My){ pi[2, j] <- runif(n = 1, min = 0.01, max = 0.98 - pi[1,j]) pi[3, j] <- 1 - sum(pi[,j]) } ## Simulate Data for(k in 1:G){ data.N1[k,] <- 2^rnorm(M1, MuN1[k], SigmaN1[k]); # normal reference 1 data.N2[k,] <- 2^rnorm(M2, MuN2[k], SigmaN2[k]); # normal reference 1 True.data.T[k,] <- 2^rnorm(My, MuT[k], SigmaT[k]); # True Tumor True.data.N1[k,] <- 2^rnorm(My, MuN1[k], SigmaN1[k]); # True Normal 1 True.data.N2[k,] <- 2^rnorm(My, MuN2[k], SigmaN2[k]); # True Normal 1 data.Y[k,] <- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.N2[k,] + pi[3,]*True.data.T[k,] # Mixture Tumor } ```` where $G1$ is the number of genes that $\mu_{N1}$ is close to $\mu_{N2}$. ```{r read_data_3comp, warning=FALSE, message=FALSE} data("test.data.3comp") # res.S1 <- DeMixT_DE(data.Y = test.data.3comp$data.Y, data.N1 = test.data.3comp$data.N1, # data.N2 = test.data.3comp$data.N2, if.filter = TRUE) load('Res_3comp/res.S1.RData'); ``` ```{r sim_3comp, fig.height = 4, fig.width = 6, fig.align='center', warning=FALSE} res.3comp= as.data.frame(cbind(round(t(matrix(t(test.data.3comp$pi), nrow = 1)),2), round(t(matrix(t(res.S1$pi), nrow = 1)),2), rep(c('N1','N2','T'), each = 20))) res.3comp$V1 <- as.numeric(as.character(res.3comp$V1)) res.3comp$V2 <- as.numeric(as.character(res.3comp$V2)) res.3comp$V3 = as.factor(res.3comp$V3) names(res.3comp) = c('True.Proportion', 'Estimated.Proportion', 'Component') ## Plot ggplot(res.3comp, aes(x=True.Proportion, y=Estimated.Proportion, group = Component, color=Component, shape=Component)) + geom_point() + geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "black", lwd = 0.5) + xlim(0,1) + ylim(0,1) + scale_shape_manual(values=c(seq(1:3))) + labs(x = 'True Proportion', y = 'Estimated Proportion') ``` ## 6.4 Real data: PRAD in TCGA dataset Here, we use a subset of the bulk RNAseq data of prostate adenocarcinoma (PRAD) from TCGA (https://portal.gdc.cancer.gov/) as an example. The analysis pipeline consists of the following steps: - Obtaining raw read counts for the tumor and normal RNAseq data - Loading libraries and data - Data preprocessing - Deconvolution using DeMixT ### 6.4.1 Obtain raw read counts for the tumor and normal RNAseq data The raw read counts for the tumor and normal samples from TCGA PRAD are downloaded from [TCGA data portal](https://portal.gdc.cancer.gov/). One can also generate the raw read counts from fastq or bam files by following the [GDC mRNA Analysis Pipeline](https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/). ### 6.4.2 Obtain raw read counts for the tumor and normal RNAseq data Load input data (available at [PRAD.RData](https://wwylab.github.io/DeMixT/etc/PRAD.RData)) ```{r load_data, fig.height = 4, fig.width = 6, fig.align='center', warning=FALSE} load(url("https://wwylab.github.io/DeMixT/etc/PRAD.RData")) ``` Three data are included in the `PRAD.RData` file. - `PRAD`: Read counts matrix (gene x sample) with genes as row names and sample ids as column names. - `Normal.id`: TCGA ids of PRAD normal samples. - `Tumor.id` TCGA ids of PRAD tumor samples. A glimpse of `PRAD`: ```{r view_data, fig.height = 4, fig.width = 6, fig.align='center', warning=FALSE} head(PRAD[,1:5]) cat('Number of genes: ', dim(PRAD)[1], '\n') cat('Number of normal sample: ', length(Normal.id), '\n') cat('Number of tumor sample: ', length(Tumor.id), '\n') ``` ### 6.4.3 Data preprocessing Conduct data cleaning and normalization before running DeMixT. ``` PRAD = PRAD[, c(Normal.id, Tumor.id)] selected.genes = 9000 cutoff_normal_range = c(0.1, 1.0) cutoff_tumor_range = c(0, 2.5) cutoff_step = 0.1 preprocessed_data = DeMixT_preprocessing(PRAD, Normal.id, Tumor.id, selected.genes, cutoff_normal_range, cutoff_tumor_range, cutoff_step) PRAD_filter = preprocessed_data$count.matrix sd_cutoff_normal = preprocessed_data$sd_cutoff_normal sd_cutoff_tumor = preprocessed_data$sd_cutoff_tumor cat("Normal sd cutoff:", preprocessed_data$sd_cutoff_normal, "\n") cat("Tumor sd cutoff:", preprocessed_data$sd_cutoff_tumor, "\n") cat('Number of genes after filtering: ', dim(PRAD_filter)[1], '\n') ``` The function `DeMixT_preprocessing` identifies two intervals based on the standard deviation of the log-transformed gene expression for normal and tumor samples, respectively, within the pre-defined ranges (`cutoff_normal_range` and `cutoff_tumor_range`). In this example, we choose to select about 9000 genes before running DeMixT with the GS (Gene Selection) method to ensure that our model-based gene selection maintains good statistical properties. `DeMixT_preprocessing` outputs a list object called `preprocessed_data` which contains: - `preprocessed_data$count.matrix`: Preprocesssed count matrix - `preprocessed_data$sd_cutoff_normal`: Actual cut-off value when desired number of genes are selected for normal samples - `preprocessed_data$sd_cutoff_tumor`: Actual cut-off value when desired number of genes are selected for tumor samples ### 6.4.4 Deconvolution using DeMixT To optimize the parameters in `DeMixT` for input data, we recommend testing an array of combinations of number of spike-ins and number of selected genes. The number of CPU cores used by the `DeMixT` function for parallel computing is specified by the parameter `nthread`. By default, `nthread = total_number_of_cores_on_the_machine - 1`. Users can adjust `nthread` to any number between 0 and the total number of cores available on the machine. For reference, `DeMixT` takes approximately 3-4 minutes to process the PRAD data in this tutorial for each parameter combination when `nthread` is set to 55. ``` # Due to the random initial values and the spike-in samples used in the DeMixT function, # we recommand that users set seeds to ensure reproducibility. # This seed setting will be incorporated internally in DeMixT in the next update. set.seed(1234) data.Y = SummarizedExperiment(assays = list(counts = PRAD_filter[, Tumor.id])) data.N1 <- SummarizedExperiment(assays = list(counts = PRAD_filter[, Normal.id])) # In practice, we set the maximum number of spike-in as min(n/3, 200), # where n is the number of samples. nspikesin_list = c(0, 5, 10) # One may set a wider range than provided below for studies other than TCGA. ngene.selected_list = c(500, 1000, 1500, 2500) for(nspikesin in nspikesin_list){ for(ngene.selected in ngene.selected_list){ name = paste("PRAD_demixt_GS_res_nspikesin", nspikesin, "ngene.selected", ngene.selected, sep = "_"); name = paste(name, ".RData", sep = ""); res = DeMixT(data.Y = data.Y, data.N1 = data.N1, ngene.selected.for.pi = ngene.selected, ngene.Profile.selected = ngene.selected, filter.sd = 0.7, # We recommand to use upper bound of gene expression standard deviation # for normal reference. i.e., preprocessed_data$sd_cutoff_normal[2] gene.selection.method = "GS", nspikein = nspikesin) save(res, file = name) } } ``` **Note:** We use a profiling likelihood-based method to select genes, during which we calculate confidence intervals for the model parameters using the inverse of the Hessian matrix. When the input data (e.g., gene expression levels from spatial transcriptomic data) is sparse, the Hessian matrix will contain infinite values, hence those confidence intervals can’t be calculated. In this case, gene selection will be performed through differential expression analysis (identical to `DeMix_DE`). This alternative is automatically performed inside `DeMix_GS` when the above situation happens. ``` PiT_GS_PRAD <- c() row_names <- c() for(nspikesin in nspikesin_list){ for(ngene.selected in ngene.selected_list){ name_simplify <- paste(nspikesin, ngene.selected, sep = "_") row_names <- c(row_names, name_simplify) name = paste("PRAD_demixt_GS_res_nspikesin", nspikesin, "ngene.selected", ngene.selected, sep = "_"); name = paste(name, ".RData", sep = "") load(name) PiT_GS_PRAD <- cbind(PiT_GS_PRAD, res$pi[2, ]) } } colnames(PiT_GS_PRAD) <- row_names ``` This step saves the deconvolution results (PiT) into a dataframe with columns named after the combination of the number of spike-ins and number of genes selected. Then one can calculate and plot the pairwise correlations of estimated tumor proportions across different parameter combinations as shown below. ``` pairs.panels(PiT_GS_PRAD, method = "spearman", # correlation method hist.col = "#00AFBB", density = TRUE, # show density plots ellipses = TRUE, # show correlation ellipses main = 'Correlations of Tumor Proportions with GS between Different Parameter Combination', xlim = c(0,1), ylim = c(0,1)) ``` ```{r correlation, echo=FALSE, out.width='100%'} knitr::include_graphics(path = paste0("pairwise_correlation.png")) ``` Print out the average pairwise correlation of tumor proportions across different parameter combinations. ``` PiT_GS_PRAD <- as.data.frame(PiT_GS_PRAD) Spearman_correlations <- list() for(entry_1 in colnames(PiT_GS_PRAD)) { cor.values <- c() for (entry_2 in colnames(PiT_GS_PRAD)) { if (entry_1 == entry_2) next cor.values <- c(cor.values, cor(PiT_GS_PRAD[, entry_1], PiT_GS_PRAD[, entry_2], method = "spearman")) } Spearman_correlations[[entry_1]] <- mean(cor.values) } Spearman_correlations <- unlist(Spearman_correlations) Spearman_correlations <- data.frame(num.spikein_num.selected.gene=names(Spearman_correlations), mean.correlation=Spearman_correlations) Spearman_correlations ``` The average correlation coefficient coefficients are listed below. ``` num.spikein_num.selected.gene mean.correlation 0_500 0.8641319 0_1000 0.9453534 0_1500 0.9401355 0_2500 0.9375468 5_500 0.9207604 5_1000 0.9542926 5_1500 0.9460006 5_2500 0.8992011 10_500 0.9237941 10_1000 0.9357266 10_1500 0.9249267 10_2500 0.9002124 ``` We suggest selecting the optimal parameter combination that produces the highest average correlation of estimated tumor proportions. Additionally, users are encouraged to evaluate the skewness of the PiT estimation distribution compared to a normal distribution centered around 0.5, as significant skewness may indicate biased estimation. Based on these criteria, `spike-ins = 5` and `number of selected genes = 1000` are identified as the optimal parameter combination. Using these parameters, we can obtain the corresponding tumor proportions for each sample. ``` data.frame(sample.id=Tumor.id, PiT=PiT_GS_PRAD[['5_1000']]) sample.id PiT TCGA-2A-A8VL-01A 0.7596888 TCGA-2A-A8VO-01A 0.8421716 TCGA-2A-A8VT-01A 0.8662378 TCGA-2A-A8VV-01A 0.7616749 TCGA-2A-A8W1-01A 0.8291091 TCGA-2A-A8W3-01A 0.8159406 TCGA-CH-5737-01A 0.7314935 TCGA-CH-5738-01A 0.4614545 TCGA-CH-5739-01A 0.6349423 TCGA-CH-5740-01A 0.7095117 ``` List the tumor specific expression ``` ## Load the corresponding deconvolved gene expression load("PRAD_demixt_GS_res_nspikesin_5_ngene.selected_1000.RData") res$ExprT[1:5, 1:5] TCGA-2A-A8VL-01A TCGA-2A-A8VO-01A TCGA-2A-A8VT-01A TCGA-2A-A8VV-01A TCGA-2A-A8W1-01A DPM1 1710.194 1466.484 1680.4562 1644.944 1812.600 FUCA2 3782.990 4083.382 961.0578 4165.612 1896.901 GCLC 2382.106 1826.957 1527.4895 1409.707 1913.784 LAS1L 3329.766 2758.414 3520.9410 2834.415 2530.621 ENPP4 2099.591 3123.365 3173.3516 2856.371 7413.330 ``` Instead of selecting using the parameter combination with the highest correlation, one can also select the parameter combination that produces estimated tumor proportions that are most biologically meaningful. The estimated tumor-specific proportions (PiT) can be used to calculate TmS. See our [TmS tutorial](https://wwylab.github.io/TmS/articles/TmS.html). ## 6.5 Deconvolution using normal reference samples from GTEx We conducted experiments across cancer types to evaluate the impact of technical artifacts such as batch effects to the proportion estimation when using a different cohort. We applied GTEx expression data from normal prostate samples as the normal reference to deconvolute the TCGA prostate cancer samples, where normal tissues were selected without significant pathology. The estimated proportions showed a reasonable correlation (Spearman correlation coefficient = 0.65) with those generated using TCGA normal prostate samples as the normal reference. ````markdown ## Deconvolute TCGA prostate cancer samples from GTEx normal samples res.GS.GTEx = DeMixT_GS(data.Y = TCGA_PRAD_Tumor, data.N1 = GTEx_PRAD_Normal, niter = 50, nbin = 50, nspikein = 49, filter.sd = 0.6, if.filter = TRUE, ngene.Profile.selected = 1500, mean.diff.in.CM = 0.25, ngene.selected.for.pi = 1500, tol = 10^(-5)) ## Deconvolute TCGA prostate cancer samples from TCGA normal samples res.GS.TCGA = DeMixT_GS(data.Y = TCGA_PRAD_Tumor, data.N1 = TCGA_PRAD_Normal, niter = 50, nbin = 50, nspikein = 49, filter.sd = 0.6, if.filter = TRUE, ngene.Profile.selected = 1500, mean.diff.in.CM = 0.25, ngene.selected.for.pi = 1500, tol = 10^(-5)) ```` ```{r GTEx, echo=FALSE, out.width='65%', fig.align='center'} knitr::include_graphics(path = paste0("GTEx_normal.png")) ``` # 7. Reference [1]. Wang, Z. et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience 9, 451–460 (2018). # 8. Session Info ```{r} sessionInfo(package = "DeMixT") ``` DeMixT/inst/doc/demixt.html0000644000175200017520001213257314710276060016651 0ustar00biocbuildbiocbuild A Vignette for DeMixT

A Vignette for DeMixT

Last updated: 2024-10-29

1. Introduction

Transcriptomic deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We develop a three-component deconvolution model, DeMixT, for expression data from a mixture of cancerous tissues, infiltrating immune cells and tumor microenvironment. DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components.

DeMixT is a frequentist-based method and fast in yielding accurate estimates of cell proportions and compart-ment-specific expression profiles for two-component three-component deconvolution problem. Our method promises to provide deeper insight into cancer biomarkers and assist in the development of novel prognostic markers and therapeutic strategies.

The function DeMixT is designed to finish the whole pipeline of deconvolution for two or three components. The newly added DeMixT_GS function is designed to estimates the proportions of mixed samples for each mixing component based on a new approach to select genes more effectively that utilizes profile likelihood. DeMixT_DE function is designed to estimate the proportions of all mixed samples for each mixing component based on the gene differential expressions to select genes. DeMixT_S2 function is designed to estimate the component-specific deconvolved expressions of individual mixed samples for a given set of genes.

2 Feature Description

The DeMixT R-package builds the transcriptomic deconvolution with a couple of novel features into R-based standard analysis pipeline through Bioconductor. DeMixT showed high accuracy and efficiency from our designed experiment. Hence, DeMixT can be considered as an important step towards linking tumor transcriptomic data with clinical outcomes.

Different from most previous computational deconvolution methods, DeMixT has integrated new features for the deconvolution with more than 2 components.

Joint estimation: jointly estimate component proportions and expression profiles for individual samples by requiring reference samples instead of reference genes; For the three-component deconvolution considering immune infiltration, it provides a comprehensive view of tumor-stroma-immune transcriptional dynamics, as compared to methods that address only immune subtypes within the immune component, in each tumor sample.

Efficient estimation: DeMixT adopts an approach of iterated conditional modes (ICM) to guarantee a rapid convergence to a local maximum. We also design a novel gene-set-based component merging approach to reduce the bias of proportion estimation for three-component deconvolutionthe.

Parallel computing: OpenMP enable parallel computing on single computer by taking advantage of the multiple cores shipped on modern CPUs. The ICM framework further enables parallel computing, which helps compensate for the expensive computing time used in the repeated numerical double integrations.

3. Installation

The DeMixT package is compatible with Windows, Linux and MacOS. The user can install it from Bioconductor:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("DeMixT")

For Linux and MacOS, the user can also install the latest DeMixT from GitHub:

if (!require("devtools", quietly = TRUE))
    install.packages('devtools')

devtools::install_github("wwylab/DeMixT")

Check if DeMixT is installed successfully:

# load package
library(DeMixT)

Note: DeMixT relies on OpenMP for parallel computing. Starting from R 4.00, R no longer supports OpenMP on MacOS, meaning the user can only run DeMixT with one core on MacOS. We therefore recommend the users to mainly use Linux system for running DeMixT to take advantage of the multi-core parallel computation.

4. Functions

The following table shows the functions included in DeMixT.

Table Header Second Header
DeMixT Deconvolution of tumor samples with two or three components.
DeMixT_GS Estimates the proportions of mixed samples for each mixing component based on a new approach to select genes that utilizes profile likelihood.
DeMixT_DE Estimates the proportions of mixed samples for each mixing component.
DeMixT_S2 Deconvolves expressions of each sample for unknown component.
Optimum_KernelC Call the C function used for parameter estimation in DeMixT.
DeMixT_Preprocessing Preprocessing functions before running DeMixT.

5. Methods

5.1 Model

Let \(Y_{ig}\) be the observed expression levels of the raw measured data from clinically derived malignant tumor samples for gene \(g, g = 1, \cdots, G\) and sample \(i, i = 1, \cdots, My\). \(G\) denotes the total number of probes/genes and \(My\) denotes the number of samples. The observed expression levels for solid tumors can be modeled as a linear combination of raw expression levels from three components: \[ {Y_{ig}} = \pi _{1,i}N_{1,ig} + \pi _{2,i}N_{2,ig} + (1 - \pi_{1,i} - \pi _{2,i}){T_{ig}} \label{eq:1} \]

Here \(N_{1,ig}\), \(N_{2,ig}\) and \({T_{ig}}\) are the unobserved raw expression levels from each of the three components. We call the two components for which we require reference samples the \(N_1\)-component and the \(N_2\)-component. We call the unknown component the T-component. We let \(\pi_{1,i}\) denote the proportion of the \(N_1\)-component, \(\pi_{2,i}\) denote the proportion of the \(N_2\)-component, and \(1 - \pi_{1,i}-\pi_{2,i}\) denote the proportion of the T-component. We assume that the mixing proportions of one specific sample remain the same across all genes.

Our model allows for one component to be unknown, and therefore does not require reference profiles from all components. A set of samples for \(N_{1,ig}\) and \(N_{2,ig}\), respectively, needs to be provided as input data. This three-component deconvolution model is applicable to the linear combination of any three components in any type of material. It can also be simplified to a two-component model, assuming there is just one \(N\)-component. For application in this paper, we consider tumor (\(T\)), stromal (\(N_1\)) and immune components (\(N_2\)) in an admixed sample (\(Y\)).

Following the convention that \(\log_2\)-transformed microarray gene expression data follow a normal distribution, we assume that the raw measures \(N_{1,ig} \sim LN({\mu _{{N_1}g}},\sigma _{{N_1}g}^2)\), \(N_{2,ig} \sim LN({\mu _{{N_2}g}},\sigma _{{N_2}g}^2)\) and \({T_{ig}} \sim LN({\mu _{Tg}}, \sigma _{Tg}^2)\), where LN denotes a \(\log_2\)-normal distribution and \(\sigma _{{N_1}g}^2\),\(\sigma _{{N_2}g}^2\), \(\sigma _{Tg}^2\) reflect the variations under \(\log_2\)-transformed data. Consequently, our model can be expressed as the convolution of the density function for three \(\log_2\)-normal distributions. Because there is no closed form of this convolution, we use numerical integration to evaluate the complete likelihood function (see the full likelihood in the Supplementary Materials in [1]).

5.2 The DeMixT algorithm for deconvolution

DeMixT estimates all distribution parameters and cellular proportions and reconstitutes the expression profiles for all three components for each gene and each sample. The estimation procedure (summarized in Figure 1b) has two main steps as follows.

  1. Obtain a set of parameters \(\{\pi_{1,i}, \pi_{2,i}\}_{i=1}^{My}\), \(\{\mu_T, \sigma_T\}_{g=1}^G\) to maximize the complete likelihood function, for which \(\{\mu_{N_{1,g}}, \sigma_{N_{1,g}}, \mu_{N_{2,g}}, \sigma_{N_{2,g}}\}_{g=1}^G\) were already estimated from the available unmatched samples of the \(N_1\) and \(N_2\) component tissues. (See further details in our paper.)

  2. Reconstitute the expression profiles by searching each set of \(\{n_{1,ig}, n_{2,ig}\}\) that maximizes the joint density of \(N_{1,ig}\), \(N_{2,ig}\) and \(T_{ig}\). The value of \(t_{ig}\) is solved as \({y_{ig}} - {{\hat \pi }_{1,i}}{n_{1,ig}} - {{\hat \pi }_{2,i}}{n_{2,ig}}\).

These two steps can be separately implemented using the function DeMixT_DE or DeMixT_GS for the first step and DeMixT_S2 for the second, which are combined in the function DeMixT(Note: DeMixT_GS is the default function for first step).

Since version 1.8.2, DeMixT added simulated normal reference samples, i.e., spike-in, based on the observed normal reference samples. It has been shown to improve accuracy in proportion estimation for the scenario where a dataset consists of samples where true tumor proportions are skewed to the high end.

6. Examples

6.1 Simulated two-component data

data("test.data.2comp")
# res.GS = DeMixT_GS(data.Y = test.data.2comp$data.Y, 
#                     data.N1 = test.data.2comp$data.N1,
#                     niter = 30, nbin = 50, nspikein = 50,
#                     if.filter = TRUE, ngene.Profile.selected = 150,
#                     mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,
#                     tol = 10^(-5))
load('Res_2comp/res.GS.RData')
head(t(res.GS$pi))
##               PiN1       PiT
## Sample 1 0.5955120 0.4044880
## Sample 2 0.2759014 0.7240986
## Sample 3 0.5401655 0.4598345
## Sample 4 0.4497041 0.5502959
## Sample 5 0.6516980 0.3483020
## Sample 6 0.4365191 0.5634809
head(res.GS$gene.name)
## [1] "Gene 418" "Gene 452" "Gene 421" "Gene 112" "Gene 154" "Gene 143"
data("test.data.2comp")
# res.S2 <- DeMixT_S2(data.Y = test.data.2comp$data.Y, 
#                     data.N1 = test.data.2comp$data.N1,
#                     data.N2 = NULL, 
#                     givenpi = c(t(res.S1$pi[-nrow(res.GS$pi),])), nbin = 50)
load('Res_2comp/res.S2.RData')
head(res.S2$decovExprT[,1:5],3)
##         Sample 1   Sample 2   Sample 3  Sample 4   Sample 5
## Gene 1 18.857446  60.727041 159.878946 92.031635  40.873852
## Gene 2  2.322481   3.390938   2.406093  2.558962   2.438189
## Gene 3 48.843631 208.166410  66.986239 38.107580 460.556751
head(res.S2$decovExprN1[,1:5],3)
##         Sample 1  Sample 2 Sample 3  Sample 4  Sample 5
## Gene 1  59.37087  71.80492  74.1755  73.55878  72.96267
## Gene 2 107.66874 131.20005 113.6376 120.35924 125.28224
## Gene 3 513.43184 669.79145 613.3042 491.09308 741.76507
head(res.S2$decovMu,3)
##            MuN1      MuT
## Gene 1 6.166484 5.924321
## Gene 2 6.677594 2.974551
## Gene 3 9.329628 7.396647
head(res.S2$decovSigma,3)
##           SigmaN   SigmaT
## Gene 1 0.2222914 1.127726
## Gene 2 0.2319681 1.614169
## Gene 3 0.1881647 1.320477

6.2 Simulated two-component data

In the simulation,

## Simulate MuN and MuT for each gene
  MuN <- rnorm(G, 7, 1.5)
  MuT <- rnorm(G, 7, 1.5)
  Mu <- cbind(MuN, MuT)
## Simulate SigmaN and SigmaT for each gene
  SigmaN <- runif(n = G, min = 0.1, max = 0.8)
  SigmaT <- runif(n = G, min = 0.1, max = 0.8)
## Simulate Tumor Proportion
  PiT = truncdist::rtrunc(n = My,
                          spec = 'norm', 
                          mean = 0.55,
                          sd = 0.2,
                          a = 0.25,
                          b = 0.95)

## Simulate Data
  for(k in 1:G){
    
    data.N1[k,] <- 2^rnorm(M1, MuN[k], SigmaN[k]); # normal reference
    
    True.data.T[k,] <- 2^rnorm(My, MuT[k], SigmaT[k]);  # True Tumor
    
    True.data.N1[k,] <- 2^rnorm(My, MuN[k], SigmaN[k]);  # True Normal
    
    data.Y[k,] <- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.T[k,] # Mixture Tumor
    
  }

where \(\pi_i \in (0.25, 0.95)\) is from truncated normal distribution. In general, the true distribution of tumor proportion does not follow a uniform distribution between \([0,1]\), but instead skewed to the upper part of the interval.

# ## DeMixT_DE without Spike-in Normal
# res.S1 = DeMixT_DE(data.Y = test.data.2comp$data.Y, 
#                    data.N1 = test.data.2comp$data.N1,
#                    niter = 30, nbin = 50, nspikein = 0,
#                    if.filter = TRUE, 
#                    mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,
#                    tol = 10^(-5))
# ## DeMixT_DE with Spike-in Normal
# res.S1.SP = DeMixT_DE(data.Y = test.data.2comp$data.Y, 
#                      data.N1 = test.data.2comp$data.N1,
#                      niter = 30, nbin = 50, nspikein = 50,
#                      if.filter = TRUE, 
#                      mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,
#                      tol = 10^(-5))
# ## DeMixT_GS with Spike-in Normal
# res.GS.SP = DeMixT_GS(data.Y = test.data.2comp$data.Y,
#                      data.N1 = test.data.2comp$data.N1,
#                      niter = 30, nbin = 50, nspikein = 50,
#                      if.filter = TRUE, ngene.Profile.selected = 150,
#                      mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,
#                      tol = 10^(-5))
load('Res_2comp/res.S1.RData'); load('Res_2comp/res.S1.SP.RData'); 
load('Res_2comp/res.GS.RData'); load('Res_2comp/res.GS.SP.RData');

This simulation was designed to compare previous DeMixT resutls with DeMixT spike-in results under both gene selection method.

res.2comp = as.data.frame(cbind(round(rep(t(test.data.2comp$pi[2,]),3),2), 
                                round(c(t(res.S1$pi[2,]),t(res.S1.SP$pi[2,]), t(res.GS.SP$pi[2,])),2),
                                rep(c('DE','DE-SP','GS-SP'), each = 100)), num = 1:2)
res.2comp$V1 <- as.numeric(as.character(res.2comp$V1))
res.2comp$V2 <- as.numeric(as.character(res.2comp$V2))
res.2comp$V3 = as.factor(res.2comp$V3)
names(res.2comp) = c('True.Proportion', 'Estimated.Proportion', 'Method')
## Plot
ggplot(res.2comp, aes(x=True.Proportion, y=Estimated.Proportion, group = Method, color=Method, shape=Method)) +
  geom_point() + 
  geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "black", lwd = 0.5) +
  xlim(0,1) + ylim(0,1)  +
  scale_shape_manual(values=c(seq(1:3))) +
  labs(x = 'True Proportion', y = 'Estimated Proportion')

6.3 Simulated three-component data

In this simulation,

G <- G1 + G2
## Simulate MuN1, MuN2 and MuT for each gene
  MuN1 <- rnorm(G, 7, 1.5)
  MuN2_1st <- MuN1[1:G1] + truncdist::rtrunc(n = 1, 
                                             spec = 'norm',
                                             mean = 0,
                                             sd = 1.5,
                                             a = -0.1, 
                                             b = 0.1)
  MuN2_2nd <- c()
  for(l in (G1+1):G){
    tmp <- MuN1[l] + truncdist::rtrunc(n = 1, 
                                       spec = 'norm',
                                       mean = 0,
                                       sd = 1.5,
                                       a = 0.1, 
                                       b = 3)^rbinom(1, size=1, prob=0.5)
    while(tmp <= 0) tmp <- MuN1[l] + truncdist::rtrunc(n = 1, 
                                                       spec = 'norm',
                                                       mean = 0,
                                                       sd = 1.5,
                                                       a = 0.1, 
                                                       b = 3)^rbinom(1, size=1, prob=0.5)
    MuN2_2nd <- c(MuN2_2nd, tmp)
  }
## Simulate SigmaN1, SigmaN2 and SigmaT for each gene
  SigmaN1 <- runif(n = G, min = 0.1, max = 0.8)
  SigmaN2 <- runif(n = G, min = 0.1, max = 0.8)
  SigmaT <- runif(n = G, min = 0.1, max = 0.8)
## Simulate Tumor Proportion
  pi <- matrix(0, 3, My)
  pi[1,] <- runif(n = My, min = 0.01, max = 0.97)
  for(j in 1:My){
    pi[2, j] <- runif(n = 1, min = 0.01, max = 0.98 - pi[1,j])
    pi[3, j] <- 1 - sum(pi[,j])
  }
## Simulate Data
  for(k in 1:G){
    
    data.N1[k,] <- 2^rnorm(M1, MuN1[k], SigmaN1[k]); # normal reference 1
    
    data.N2[k,] <- 2^rnorm(M2, MuN2[k], SigmaN2[k]); # normal reference 1
    
    True.data.T[k,] <- 2^rnorm(My, MuT[k], SigmaT[k]);  # True Tumor
    
    True.data.N1[k,] <- 2^rnorm(My, MuN1[k], SigmaN1[k]);  # True Normal 1
    
    True.data.N2[k,] <- 2^rnorm(My, MuN2[k], SigmaN2[k]);  # True Normal 1
    
    data.Y[k,] <- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.N2[k,] +
      pi[3,]*True.data.T[k,] # Mixture Tumor
    
  }

where \(G1\) is the number of genes that \(\mu_{N1}\) is close to \(\mu_{N2}\).

data("test.data.3comp")
# res.S1 <- DeMixT_DE(data.Y = test.data.3comp$data.Y, data.N1 = test.data.3comp$data.N1,
#                    data.N2 = test.data.3comp$data.N2, if.filter = TRUE)
load('Res_3comp/res.S1.RData'); 
res.3comp= as.data.frame(cbind(round(t(matrix(t(test.data.3comp$pi), nrow = 1)),2), 
                                round(t(matrix(t(res.S1$pi), nrow = 1)),2), 
                                rep(c('N1','N2','T'), each = 20)))
res.3comp$V1 <- as.numeric(as.character(res.3comp$V1))
res.3comp$V2 <- as.numeric(as.character(res.3comp$V2))
res.3comp$V3 = as.factor(res.3comp$V3)
names(res.3comp) = c('True.Proportion', 'Estimated.Proportion', 'Component')
## Plot
ggplot(res.3comp, aes(x=True.Proportion, y=Estimated.Proportion, group = Component, color=Component, shape=Component)) +
  geom_point() + 
  geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "black", lwd = 0.5) +
  xlim(0,1) + ylim(0,1)  +
  scale_shape_manual(values=c(seq(1:3))) +
  labs(x = 'True Proportion', y = 'Estimated Proportion')

6.4 Real data: PRAD in TCGA dataset

Here, we use a subset of the bulk RNAseq data of prostate adenocarcinoma (PRAD) from TCGA (https://portal.gdc.cancer.gov/) as an example. The analysis pipeline consists of the following steps:

  • Obtaining raw read counts for the tumor and normal RNAseq data
  • Loading libraries and data
  • Data preprocessing
  • Deconvolution using DeMixT

6.4.1 Obtain raw read counts for the tumor and normal RNAseq data

The raw read counts for the tumor and normal samples from TCGA PRAD are downloaded from TCGA data portal. One can also generate the raw read counts from fastq or bam files by following the GDC mRNA Analysis Pipeline.

6.4.2 Obtain raw read counts for the tumor and normal RNAseq data

Load input data (available at PRAD.RData)

load(url("https://wwylab.github.io/DeMixT/etc/PRAD.RData"))

Three data are included in the PRAD.RData file.

  • PRAD: Read counts matrix (gene x sample) with genes as row names and sample ids as column names.
  • Normal.id: TCGA ids of PRAD normal samples.
  • Tumor.id TCGA ids of PRAD tumor samples.

A glimpse of PRAD:

head(PRAD[,1:5])
##          TCGA-CH-5761-11A TCGA-CH-5767-11B TCGA-EJ-7115-11A TCGA-EJ-7123-11A
## TSPAN6               3876             7095             5542             2747
## TNMD                   14               51               13               24
## DPM1                 1162             2665             1544             1974
## SCYL3                 777             1517             1096             1231
## C1orf112              136              343              214              280
## FGR                   230              511              263              755
##          TCGA-EJ-7125-11A
## TSPAN6               8465
## TNMD                   63
## DPM1                 2984
## SCYL3                1514
## C1orf112              339
## FGR                   262
cat('Number of genes: ', dim(PRAD)[1], '\n')
## Number of genes:  59427
cat('Number of normal sample: ', length(Normal.id), '\n')
## Number of normal sample:  20
cat('Number of tumor sample: ', length(Tumor.id), '\n')
## Number of tumor sample:  30

6.4.3 Data preprocessing

Conduct data cleaning and normalization before running DeMixT.

PRAD = PRAD[, c(Normal.id, Tumor.id)]
selected.genes = 9000
cutoff_normal_range = c(0.1, 1.0)
cutoff_tumor_range = c(0, 2.5)
cutoff_step = 0.1

preprocessed_data = DeMixT_preprocessing(PRAD,
                                         Normal.id,
                                         Tumor.id,
                                         selected.genes,
                                         cutoff_normal_range,
                                         cutoff_tumor_range,
                                         cutoff_step)
PRAD_filter = preprocessed_data$count.matrix
sd_cutoff_normal = preprocessed_data$sd_cutoff_normal
sd_cutoff_tumor = preprocessed_data$sd_cutoff_tumor

cat("Normal sd cutoff:", preprocessed_data$sd_cutoff_normal, "\n")
cat("Tumor sd cutoff:", preprocessed_data$sd_cutoff_tumor, "\n")
cat('Number of genes after filtering: ', dim(PRAD_filter)[1], '\n')

The function DeMixT_preprocessing identifies two intervals based on the standard deviation of the log-transformed gene expression for normal and tumor samples, respectively, within the pre-defined ranges (cutoff_normal_range and cutoff_tumor_range). In this example, we choose to select about 9000 genes before running DeMixT with the GS (Gene Selection) method to ensure that our model-based gene selection maintains good statistical properties.

DeMixT_preprocessing outputs a list object called preprocessed_data which contains:

  • preprocessed_data$count.matrix: Preprocesssed count matrix
  • preprocessed_data$sd_cutoff_normal: Actual cut-off value when desired number of genes are selected for normal samples
  • preprocessed_data$sd_cutoff_tumor: Actual cut-off value when desired number of genes are selected for tumor samples

6.4.4 Deconvolution using DeMixT

To optimize the parameters in DeMixT for input data, we recommend testing an array of combinations of number of spike-ins and number of selected genes.

The number of CPU cores used by the DeMixT function for parallel computing is specified by the parameter nthread. By default, nthread = total_number_of_cores_on_the_machine - 1. Users can adjust nthread to any number between 0 and the total number of cores available on the machine. For reference, DeMixT takes approximately 3-4 minutes to process the PRAD data in this tutorial for each parameter combination when nthread is set to 55.

# Due to the random initial values and the spike-in samples used in the DeMixT function,
# we recommand that users set seeds to ensure reproducibility.
# This seed setting will be incorporated internally in DeMixT in the next update.

set.seed(1234)

data.Y = SummarizedExperiment(assays = list(counts = PRAD_filter[, Tumor.id]))
data.N1 <- SummarizedExperiment(assays = list(counts = PRAD_filter[, Normal.id]))

# In practice, we set the maximum number of spike-in as min(n/3, 200),
# where n is the number of samples.
nspikesin_list = c(0, 5, 10)
# One may set a wider range than provided below for studies other than TCGA.
ngene.selected_list = c(500, 1000, 1500, 2500)

for(nspikesin in nspikesin_list){
    for(ngene.selected in ngene.selected_list){
        name = paste("PRAD_demixt_GS_res_nspikesin", nspikesin, "ngene.selected",
                      ngene.selected,  sep = "_");
        name = paste(name, ".RData", sep = "");
        res = DeMixT(data.Y = data.Y,
                     data.N1 = data.N1,
                     ngene.selected.for.pi = ngene.selected,
                     ngene.Profile.selected = ngene.selected,
                     filter.sd = 0.7, # We recommand to use upper bound of gene expression standard deviation
                     # for normal reference. i.e., preprocessed_data$sd_cutoff_normal[2]
                     gene.selection.method = "GS",
                     nspikein = nspikesin)
        save(res, file = name)
    }
}

Note: We use a profiling likelihood-based method to select genes, during which we calculate confidence intervals for the model parameters using the inverse of the Hessian matrix. When the input data (e.g., gene expression levels from spatial transcriptomic data) is sparse, the Hessian matrix will contain infinite values, hence those confidence intervals can’t be calculated. In this case, gene selection will be performed through differential expression analysis (identical to DeMix_DE). This alternative is automatically performed inside DeMix_GS when the above situation happens.

PiT_GS_PRAD <- c()
row_names <- c()

for(nspikesin in nspikesin_list){
    for(ngene.selected in ngene.selected_list){
        name_simplify <- paste(nspikesin, ngene.selected,  sep = "_")
        row_names <- c(row_names, name_simplify)
        
        name = paste("PRAD_demixt_GS_res_nspikesin", nspikesin, 
                      "ngene.selected", ngene.selected,  sep = "_");
        name = paste(name, ".RData", sep = "")
        load(name)
        PiT_GS_PRAD <- cbind(PiT_GS_PRAD, res$pi[2, ])
    }
}
colnames(PiT_GS_PRAD) <- row_names

This step saves the deconvolution results (PiT) into a dataframe with columns named after the combination of the number of spike-ins and number of genes selected. Then one can calculate and plot the pairwise correlations of estimated tumor proportions across different parameter combinations as shown below.

pairs.panels(PiT_GS_PRAD,
            method = "spearman", # correlation method
            hist.col = "#00AFBB",
            density = TRUE,  # show density plots
            ellipses = TRUE, # show correlation ellipses
            main = 'Correlations of Tumor Proportions with GS between Different Parameter 
            Combination',
            xlim = c(0,1),
            ylim = c(0,1))

Print out the average pairwise correlation of tumor proportions across different parameter combinations.

PiT_GS_PRAD <- as.data.frame(PiT_GS_PRAD)
Spearman_correlations <- list()

for(entry_1 in colnames(PiT_GS_PRAD)) {
  cor.values <- c()
  for (entry_2 in colnames(PiT_GS_PRAD)) {
    if (entry_1 == entry_2)
      next
    
    cor.values <- c(cor.values, 
                    cor(PiT_GS_PRAD[, entry_1], 
                    PiT_GS_PRAD[, entry_2], 
                    method = "spearman"))
  }
  
  Spearman_correlations[[entry_1]] <- mean(cor.values)
}

Spearman_correlations <- unlist(Spearman_correlations)
Spearman_correlations <- data.frame(num.spikein_num.selected.gene=names(Spearman_correlations), mean.correlation=Spearman_correlations)

Spearman_correlations

The average correlation coefficient coefficients are listed below.

num.spikein_num.selected.gene   mean.correlation
0_500   0.8641319       
0_1000  0.9453534       
0_1500  0.9401355       
0_2500  0.9375468       
5_500   0.9207604       
5_1000  0.9542926       
5_1500  0.9460006       
5_2500  0.8992011       
10_500  0.9237941       
10_1000 0.9357266   
10_1500 0.9249267       
10_2500 0.9002124

We suggest selecting the optimal parameter combination that produces the highest average correlation of estimated tumor proportions. Additionally, users are encouraged to evaluate the skewness of the PiT estimation distribution compared to a normal distribution centered around 0.5, as significant skewness may indicate biased estimation.

Based on these criteria, spike-ins = 5 and number of selected genes = 1000 are identified as the optimal parameter combination. Using these parameters, we can obtain the corresponding tumor proportions for each sample.

data.frame(sample.id=Tumor.id, PiT=PiT_GS_PRAD[['5_1000']])

sample.id               PiT
TCGA-2A-A8VL-01A    0.7596888           
TCGA-2A-A8VO-01A    0.8421716           
TCGA-2A-A8VT-01A    0.8662378           
TCGA-2A-A8VV-01A    0.7616749           
TCGA-2A-A8W1-01A    0.8291091           
TCGA-2A-A8W3-01A    0.8159406           
TCGA-CH-5737-01A    0.7314935           
TCGA-CH-5738-01A    0.4614545           
TCGA-CH-5739-01A    0.6349423           
TCGA-CH-5740-01A    0.7095117

List the tumor specific expression

## Load the corresponding deconvolved gene expression
load("PRAD_demixt_GS_res_nspikesin_5_ngene.selected_1000.RData")
res$ExprT[1:5, 1:5]

      TCGA-2A-A8VL-01A TCGA-2A-A8VO-01A TCGA-2A-A8VT-01A TCGA-2A-A8VV-01A TCGA-2A-A8W1-01A
DPM1          1710.194         1466.484        1680.4562         1644.944         1812.600
FUCA2         3782.990         4083.382         961.0578         4165.612         1896.901
GCLC          2382.106         1826.957        1527.4895         1409.707         1913.784
LAS1L         3329.766         2758.414        3520.9410         2834.415         2530.621
ENPP4         2099.591         3123.365        3173.3516         2856.371         7413.330

Instead of selecting using the parameter combination with the highest correlation, one can also select the parameter combination that produces estimated tumor proportions that are most biologically meaningful.

The estimated tumor-specific proportions (PiT) can be used to calculate TmS. See our TmS tutorial.

6.5 Deconvolution using normal reference samples from GTEx

We conducted experiments across cancer types to evaluate the impact of technical artifacts such as batch effects to the proportion estimation when using a different cohort. We applied GTEx expression data from normal prostate samples as the normal reference to deconvolute the TCGA prostate cancer samples, where normal tissues were selected without significant pathology. The estimated proportions showed a reasonable correlation (Spearman correlation coefficient = 0.65) with those generated using TCGA normal prostate samples as the normal reference.

## Deconvolute TCGA prostate cancer samples from GTEx normal samples
res.GS.GTEx = DeMixT_GS(data.Y = TCGA_PRAD_Tumor,
                        data.N1 = GTEx_PRAD_Normal,
                        niter = 50, nbin = 50, nspikein = 49, filter.sd = 0.6,
                        if.filter = TRUE, ngene.Profile.selected = 1500,
                        mean.diff.in.CM = 0.25, ngene.selected.for.pi = 1500,
                        tol = 10^(-5))
## Deconvolute TCGA prostate cancer samples from TCGA normal samples
res.GS.TCGA = DeMixT_GS(data.Y = TCGA_PRAD_Tumor,
                        data.N1 = TCGA_PRAD_Normal,
                        niter = 50, nbin = 50, nspikein = 49, filter.sd = 0.6,
                        if.filter = TRUE, ngene.Profile.selected = 1500,
                        mean.diff.in.CM = 0.25, ngene.selected.for.pi = 1500,
                        tol = 10^(-5))

7. Reference

[1]. Wang, Z. et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience 9, 451–460 (2018).

8. Session Info

sessionInfo(package = "DeMixT")
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## character(0)
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## other attached packages:
## [1] DeMixT_1.22.0
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## loaded via a namespace (and not attached):
##   [1] DBI_1.2.3                   mnormt_2.1.1               
##   [3] bitops_1.0-9                gridExtra_2.3              
##   [5] bsseq_1.42.0                permute_0.9-7              
##   [7] rlang_1.1.4                 magrittr_2.0.3             
##   [9] matrixStats_1.4.1           compiler_4.4.1             
##  [11] RSQLite_2.3.7               mgcv_1.9-1                 
##  [13] DelayedMatrixStats_1.28.0   png_0.1-8                  
##  [15] vctrs_0.6.5                 sva_3.54.0                 
##  [17] pkgconfig_2.0.3             crayon_1.5.3               
##  [19] fastmap_1.2.0               XVector_0.46.0             
##  [21] labeling_0.4.3              utf8_1.2.4                 
##  [23] Rsamtools_2.22.0            rmarkdown_2.28             
##  [25] grDevices_4.4.1             UCSC.utils_1.2.0           
##  [27] bit_4.5.0                   xfun_0.48                  
##  [29] zlibbioc_1.52.0             cachem_1.1.0               
##  [31] graphics_4.4.1              GenomeInfoDb_1.42.0        
##  [33] jsonlite_1.8.9              blob_1.2.4                 
##  [35] highr_0.11                  rhdf5filters_1.18.0        
##  [37] DelayedArray_0.32.0         Rhdf5lib_1.28.0            
##  [39] BiocParallel_1.40.0         psych_2.4.6.26             
##  [41] parallel_4.4.1              R6_2.5.1                   
##  [43] bslib_0.8.0                 limma_3.62.0               
##  [45] rtracklayer_1.66.0          genefilter_1.88.0          
##  [47] GenomicRanges_1.58.0        jquerylib_0.1.4            
##  [49] Rcpp_1.0.13                 SummarizedExperiment_1.36.0
##  [51] knitr_1.48                  base64enc_0.1-3            
##  [53] R.utils_2.12.3              IRanges_2.40.0             
##  [55] Matrix_1.7-1                splines_4.4.1              
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##  [63] lattice_0.22-6              tibble_3.2.1               
##  [65] KEGGREST_1.46.0             Biobase_2.66.0             
##  [67] withr_3.0.2                 evaluate_1.0.1             
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##  [71] Biostrings_2.74.0           pillar_1.9.0               
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##  [75] KernSmooth_2.23-24          stats4_4.4.1               
##  [77] generics_0.1.3              RCurl_1.98-1.16            
##  [79] S4Vectors_0.44.0            ggplot2_3.5.1              
##  [81] sparseMatrixStats_1.18.0    munsell_0.5.1              
##  [83] scales_1.3.0                stats_4.4.1                
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##  [97] XML_3.99-0.17               rhdf5_2.50.0               
##  [99] grid_4.4.1                  utils_4.4.1                
## [101] matrixcalc_1.0-6            methods_4.4.1              
## [103] edgeR_4.4.0                 AnnotationDbi_1.68.0       
## [105] colorspace_2.1-1            nlme_3.1-166               
## [107] GenomeInfoDbData_1.2.13     HDF5Array_1.34.0           
## [109] restfulr_0.0.15             cli_3.6.3                  
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## [117] sass_0.4.9                  digest_0.6.37              
## [119] BiocGenerics_0.52.0         SparseArray_1.6.0          
## [121] farver_2.1.2                rjson_0.2.23               
## [123] memoise_2.0.1               htmltools_0.5.8.1          
## [125] R.oo_1.26.0                 lifecycle_1.0.4            
## [127] httr_1.4.7                  statmod_1.5.0              
## [129] bit64_4.5.2
DeMixT/man/0000755000175200017520000000000014710233064013501 5ustar00biocbuildbiocbuildDeMixT/man/DeMixT.Rd0000644000175200017520000002702414710233064015127 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DeMixT.R \name{DeMixT} \alias{DeMixT} \title{Deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms} \usage{ DeMixT( data.Y, data.N1, data.N2 = NULL, niter = 10, nbin = 50, if.filter = TRUE, filter.sd = 0.5, ngene.selected.for.pi = NA, mean.diff.in.CM = 0.25, nspikein = min(200, ceiling(ncol(data.Y) * 0.3)), gene.selection.method = "GS", ngene.Profile.selected = NA, tol = 10^(-5), output.more.info = FALSE, pi01 = NULL, pi02 = NULL, nthread = parallel::detectCores() - 1 ) } \arguments{ \item{data.Y}{A SummarizedExperiment object of expression data from mixed tumor samples. It is a \eqn{G} by \eqn{My} matrix where \eqn{G} is the number of genes and \eqn{My} is the number of mixed samples. Samples with the same tissue type should be placed together in columns.} \item{data.N1}{A SummarizedExperiment object of expression data from reference component 1 (e.g., normal). It is a \eqn{G} by \eqn{M1} matrix where \eqn{G} is the number of genes and \eqn{M1} is the number of samples for component 1.} \item{data.N2}{A SummarizedExperiment object of expression data from additional reference samples. It is a \eqn{G} by \eqn{M2} matrix where \eqn{G} is the number of genes and \eqn{M2} is the number of samples for component 2. Component 2 is needed only for running a three-component model.} \item{niter}{The maximum number of iterations used in the algorithm of iterated conditional modes. A larger value better guarantees the convergence in estimation but increases the running time. The default is 10.} \item{nbin}{The number of bins used in numerical integration for computing complete likelihood. A larger value increases accuracy in estimation but increases the running time, especially in a three-component deconvolution problem. The default is 50.} \item{if.filter}{The logical flag indicating whether a predetermined filter rule is used to select genes for proportion estimation. The default is TRUE.} \item{filter.sd}{The cut-off for the standard deviation of lognormal distribution. Genes whose log transferred standard deviation smaller than the cut-off will be selected into the model. The default is 0.5.} \item{ngene.selected.for.pi}{The percentage or the number of genes used for proportion estimation. The difference between the expression levels from mixed tumor samples and the known component(s) are evaluated, and the most differential expressed genes are selected, which is called DE. It is enabled when if.filter = TRUE. The default is \eqn{min(1500, 0.3*G)}, where \eqn{G} is the number of genes. Users can also try using more genes, ranging from \eqn{0.3*G} to \eqn{0.5*G}, and evaluate the outcome.} \item{mean.diff.in.CM}{Threshold of expression difference for selecting genes in the component merging strategy. We merge three-component to two-component by selecting genes with similar expressions for the two known components. Genes with the mean differences less than the threshold will be selected for component merging. It is used in the three-component setting, and is enabled when if.filter = TRUE. The default is 0.25.} \item{nspikein}{The number of spikes in normal reference used for proportion estimation. The default value is \eqn{ min(200, 0.3*My)}, where \eqn{My} the number of mixed samples. If it is set to 0, proportion estimation is performed without any spike in normal reference.} \item{gene.selection.method}{The method of gene selection used for proportion estimation. The default method is 'GS', which applies a profile likelihood based method for gene selection. If it is set to 'DE', the most differential expressed genes are selected.} \item{ngene.Profile.selected}{The number of genes used for proportion estimation ranked by profile likelihood. The default is \eqn{min(1500,0.1*G)}, where \eqn{G} is the number of genes. This is enabled only when gene.selection.method is set to 'GS'.} \item{tol}{The convergence criterion. The default is 10^(-5).} \item{output.more.info}{The logical flag indicating whether to show the estimated proportions in each iteration in the output.} \item{pi01}{Initialized proportion for first kown component. The default is \eqn{Null} and pi01 will be generated randomly from uniform distribution.} \item{pi02}{Initialized proportion for second kown component. pi02 is needed only for running a three-component model. The default is \eqn{Null} and pi02 will be generated randomly from uniform distribution.} \item{nthread}{The number of threads used for deconvolution when OpenMP is available in the system. The default is the number of whole threads minus one. In our no-OpenMP version, it is set to 1.} } \value{ \item{pi}{A matrix of estimated proportion. First row and second row corresponds to the proportion estimate for the known components and unkown component respectively for two or three component settings, and each column corresponds to one sample.} \item{pi.iter}{Estimated proportions in each iteration. It is a \eqn{niter* My*p} array, where \eqn{p} is the number of components. This is enabled only when output.more.info = TRUE.} \item{ExprT}{A matrix of deconvolved expression profiles corresponding to T-component in mixed samples for a given subset of genes. Each row corresponds to one gene and each column corresponds to one sample.} \item{ExprN1}{A matrix of deconvolved expression profiles corresponding to N1-component in mixed samples for a given subset of genes. Each row corresponds to one gene and each column corresponds to one sample.} \item{ExprN2}{A matrix of deconvolved expression profiles corresponding to N2-component in mixed samples for a given subset of genes in a three-component setting. Each row corresponds to one gene and each column corresponds to one sample.} \item{Mu}{A matrix of estimated \eqn{Mu} of log2-normal distribution for both known (\eqn{MuN1, MuN2}) and unknown component (\eqn{MuT}). Each row corresponds to one gene.} \item{Sigma}{Estimated \eqn{Sigma} of log2-normal distribution for both known (\eqn{SigmaN1, SigmaN2}) and unknown component (\eqn{SigmaT}). Each row corresponds to one gene.} \item{gene.name}{The names of genes used in estimating the proportions. If no gene names are provided in the original data set, the genes will be automatically indexed.} \item{pi}{A matrix of estimated proportion. First row and second row corresponds to the proportion estimate for the known components and unkown component respectively for two or three component settings, and each column corresponds to one sample.} \item{pi.iter}{Estimated proportions in each iteration. It is a \eqn{niter* My*p} array, where \eqn{p} is the number of components. This is enabled only when output.more.info = TRUE.} \item{ExprT}{A matrix of deconvolved expression profiles corresponding to T-component in mixed samples for a given subset of genes. Each row corresponds to one gene and each column corresponds to one sample.} \item{ExprN1}{A matrix of deconvolved expression profiles corresponding to N1-component in mixed samples for a given subset of genes. Each row corresponds to one gene and each column corresponds to one sample.} \item{ExprN2}{A matrix of deconvolved expression profiles corresponding to N2-component in mixed samples for a given subset of genes in a three-component setting. Each row corresponds to one gene and each column corresponds to one sample.} \item{Mu}{A matrix of estimated \eqn{Mu} of log2-normal distribution for both known (\eqn{MuN1, MuN2}) and unknown component (\eqn{MuT}). Each row corresponds to one gene.} \item{Sigma}{Estimated \eqn{Sigma} of log2-normal distribution for both known (\eqn{SigmaN1, SigmaN2}) and unknown component (\eqn{SigmaT}). Each row corresponds to one gene.} \item{gene.name}{The names of genes used in estimating the proportions. If no gene names are provided in the original data set, the genes will be automatically indexed.} } \description{ DeMixT is a software that performs deconvolution on transcriptome data from a mixture of two or three components. } \examples{ # Example 1: simulated two-component data by using GS(gene selection method) data(test.data.2comp) # res <- DeMixT(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # data.N2 = NULL, nspikein = 50, # gene.selection.method = 'GS', # niter = 10, nbin = 50, if.filter = TRUE, # ngene.selected.for.pi = 150, # mean.diff.in.CM = 0.25, tol = 10^(-5)) # res$pi # head(res$ExprT, 3) # head(res$ExprN1, 3) # head(res$Mu, 3) # head(res$Sigma, 3) # # Example 2: simulated two-component data by using DE(gene selection method) # data(test.data.2comp) # res <- DeMixT(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # data.N2 = NULL, nspikein = 50, g # ene.selection.method = 'DE', # niter = 10, nbin = 50, if.filter = TRUE, # ngene.selected.for.pi = 150, # mean.diff.in.CM = 0.25, tol = 10^(-5)) # # Example 3: three-component mixed cell line data applying # component merging strategy # data(test.data.3comp) # res <- DeMixT(data.Y = test.data.3comp$data.Y, # data.N1 = test.data.3comp$data.N1, # data.N2 = test.data.3comp$data.N2, # if.filter = TRUE) # # Example: convert a matrix into the SummarizedExperiment format # library(SummarizedExperiment) # example <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3, byrow = TRUE) # example.se <- SummarizedExperiment(assays = list(counts = example)) # Example 1: simulated two-component data by using GS(gene selection method) data(test.data.2comp) # res <- DeMixT(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # data.N2 = NULL, nspikein = 50, # gene.selection.method = 'GS', # niter = 10, nbin = 50, if.filter = TRUE, # ngene.selected.for.pi = 150, # mean.diff.in.CM = 0.25, tol = 10^(-5)) # res$pi # head(res$ExprT, 3) # head(res$ExprN1, 3) # head(res$Mu, 3) # head(res$Sigma, 3) # # Example 2: simulated two-component data by using DE(gene selection method) # data(test.data.2comp) # res <- DeMixT(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # data.N2 = NULL, nspikein = 50, g # ene.selection.method = 'DE', # niter = 10, nbin = 50, if.filter = TRUE, # ngene.selected.for.pi = 150, # mean.diff.in.CM = 0.25, tol = 10^(-5)) # # Example 3: three-component mixed cell line data applying # component merging strategy # data(test.data.3comp) # res <- DeMixT(data.Y = test.data.3comp$data.Y, # data.N1 = test.data.3comp$data.N1, # data.N2 = test.data.3comp$data.N2, # if.filter = TRUE) # # Example: convert a matrix into the SummarizedExperiment format # library(SummarizedExperiment) # example <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3, byrow = TRUE) # example.se <- SummarizedExperiment(assays = list(counts = example)) } \references{ Wang Z, Cao S, Morris J S, et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience, 2018, 9: 451-460. Wang Z, Cao S, Morris J S, et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience, 2018, 9: 451-460. } \seealso{ http://bioinformatics.mdanderson.org/main/DeMixT http://bioinformatics.mdanderson.org/main/DeMixT } \author{ Zeya Wang, Wenyi Wang Zeya Wang, Wenyi Wang } \keyword{DeMixT} DeMixT/man/DeMixT_DE.Rd0000644000175200017520000001422114710233064015472 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DeMixT_DE.R \name{DeMixT_DE} \alias{DeMixT_DE} \title{Estimates the proportions of mixed samples for each mixing component} \usage{ DeMixT_DE( data.Y, data.N1, data.N2 = NULL, niter = 10, nbin = 50, if.filter = TRUE, filter.sd = 0.5, ngene.selected.for.pi = NA, nspikein = min(200, ceiling(ncol(data.Y) * 0.3)), mean.diff.in.CM = 0.25, tol = 10^(-5), pi01 = NULL, pi02 = NULL, nthread = parallel::detectCores() - 1 ) } \arguments{ \item{data.Y}{A SummarizedExperiment object of expression data from mixed tumor samples. It is a \eqn{G} by \eqn{My} matrix where \eqn{G} is the number of genes and \eqn{My} is the number of mixed samples. Samples with the same tissue type should be placed together in columns.} \item{data.N1}{A SummarizedExperiment object of expression data from reference component 1 (e.g., normal). It is a \eqn{G} by \eqn{M1} matrix where \eqn{G} is the number of genes and \eqn{M1} is the number of samples for component 1.} \item{data.N2}{A SummarizedExperiment object of expression data from additional reference samples. It is a \eqn{G} by \eqn{M2} matrix where \eqn{G} is the number of genes and \eqn{M2} is the number of samples for component 2. Component 2 is needed only for running a three-component model.} \item{niter}{The maximum number of iterations used in the algorithm of iterated conditional modes. A larger value better guarantees the convergence in estimation but increases the running time. The default is 10.} \item{nbin}{The number of bins used in numerical integration for computing complete likelihood. A larger value increases accuracy in estimation but increases the running time, especially in a three-component deconvolution problem. The default is 50.} \item{if.filter}{The logical flag indicating whether a predetermined filter rule is used to select genes for proportion estimation. The default is TRUE.} \item{filter.sd}{The cut-off for the standard deviation of lognormal distribution. Genes whose log transferred standard deviation smaller than the cut-off will be selected into the model. The default is 0.5.} \item{ngene.selected.for.pi}{The percentage or the number of genes used for proportion estimation. The difference between the expression levels from mixed tumor samples and the known component(s) are evaluated, and the most differential expressed genes are selected, which is called DE. It is enabled when if.filter = TRUE. The default is \eqn{min(1500, 0.3*G)}, where \eqn{G} is the number of genes. Users can also try using more genes, ranging from \eqn{0.3*G} to \eqn{0.5*G}, and evaluate the outcome.} \item{nspikein}{The number of spikes in normal reference used for proportion estimation. The default value is \eqn{ min(200, 0.3*My)}, where \eqn{My} the number of mixed samples. If it is set to 0, proportion estimation is performed without any spike in normal reference.} \item{mean.diff.in.CM}{Threshold of expression difference for selecting genes in the component merging strategy. We merge three-component to two-component by selecting genes with similar expressions for the two known components. Genes with the mean differences less than the threshold will be selected for component merging. It is used in the three-component setting, and is enabled when if.filter = TRUE. The default is 0.25.} \item{tol}{The convergence criterion. The default is 10^(-5).} \item{pi01}{Initialized proportion for first kown component. The default is \eqn{Null} and pi01 will be generated randomly from uniform distribution.} \item{pi02}{Initialized proportion for second kown component. pi02 is needed only for running a three-component model. The default is \eqn{Null} and pi02 will be generated randomly from uniform distribution.} \item{nthread}{The number of threads used for deconvolution when OpenMP is available in the system. The default is the number of whole threads minus one. In our no-OpenMP version, it is set to 1.} } \value{ \item{pi}{A matrix of estimated proportion. First row and second row corresponds to the proportion estimate for the known components and unkown component respectively for two or three component settings, and each column corresponds to one sample.} \item{pi.iter}{Estimated proportions in each iteration. It is a \eqn{niter *Ny*p} array, where \eqn{p} is the number of components. This is enabled only when output.more.info = TRUE.} \item{gene.name}{The names of genes used in estimating the proportions. If no gene names are provided in the original data set, the genes will be automatically indexed.} } \description{ This function is designed to estimate the deconvolved expressions of individual mixed tumor samples for unknown component for each gene. } \examples{ # Example 1: estimate proportions for simulated two-component data # with spike-in normal reference data(test.data.2comp) # res.DE = DeMixT_DE(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 10, nbin = 50, nspikein = 50, # if.filter = TRUE, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) # # Example 2: estimate proportions for simulated two-component data # without spike-in normal reference # data(test.data.2comp) # res.DE = DeMixT_DE(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 10, nbin = 50, nspikein = 0, # if.filter = TRUE, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) # # Example 3: estimate proportions for simulated three-component # mixed cell line data # data(test.data.3comp) # res.DE <- DeMixT_DE(data.Y = test.data.3comp$data.Y, # data.N1 = test.data.3comp$data.N1, # data.N2 = test.data.3comp$data.N2, # if.filter = TRUE) } \references{ Wang Z, Cao S, Morris J S, et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience, 2018, 9: 451-460. } \seealso{ http://bioinformatics.mdanderson.org/main/DeMixT } \author{ Zeya Wang, Wenyi Wang } \keyword{DeMixT_DE} DeMixT/man/DeMixT_GS.Rd0000644000175200017520000001573014710233064015521 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DeMixT_GS.R \name{DeMixT_GS} \alias{DeMixT_GS} \title{Estimates the proportions of mixed samples for each mixing component using profile likelihood gene selection} \usage{ DeMixT_GS( data.Y, data.N1, data.N2 = NULL, niter = 10, nbin = 50, if.filter = TRUE, filter.sd = 0.5, ngene.Profile.selected = NA, ngene.selected.for.pi = NA, mean.diff.in.CM = 0.25, nspikein = min(200, ceiling(ncol(data.Y) * 0.3)), tol = 10^(-5), pi01 = NULL, pi02 = NULL, nthread = parallel::detectCores() - 1 ) } \arguments{ \item{data.Y}{A SummarizedExperiment object of expression data from mixed tumor samples. It is a \eqn{G} by \eqn{My} matrix where \eqn{G} is the number of genes and \eqn{My} is the number of mixed samples. Samples with the same tissue type should be placed together in columns.} \item{data.N1}{A SummarizedExperiment object of expression data from reference component 1 (e.g., normal). It is a \eqn{G} by \eqn{M1} matrix where \eqn{G} is the number of genes and \eqn{M1} is the number of samples for component 1.} \item{data.N2}{A SummarizedExperiment object of expression data from additional reference samples. It is a \eqn{G} by \eqn{M2} matrix where \eqn{G} is the number of genes and \eqn{M2} is the number of samples for component 2. Component 2 is needed only for running a three-component model.} \item{niter}{The maximum number of iterations used in the algorithm of iterated conditional modes. A larger value better guarantees the convergence in estimation but increases the running time. The default is 10.} \item{nbin}{The number of bins used in numerical integration for computing complete likelihood. A larger value increases accuracy in estimation but increases the running time, especially in a three-component deconvolution problem. The default is 50.} \item{if.filter}{The logical flag indicating whether a predetermined filter rule is used to select genes for proportion estimation. The default is TRUE.} \item{filter.sd}{The cut-off for the standard deviation of lognormal distribution. Genes whose log transferred standard deviation smaller than the cut-off will be selected into the model. The default is TRUE.} \item{ngene.Profile.selected}{The number of genes used for proportion estimation ranked by profile likelihood. The default is \eqn{min(1500,0.1*G)}, where \eqn{G} is the number of genes.} \item{ngene.selected.for.pi}{The percentage or the number of genes used for proportion estimation. The difference between the expression levels from mixed tumor samples and the known component(s) are evaluated, and the most differential expressed genes are selected, which is called DE. It is enabled when if.filter = TRUE. The default is \eqn{min(1500, 0.3*G)}, where \eqn{G} is the number of genes. Users can also try using more genes, ranging from \eqn{0.3*G} to \eqn{0.5*G}, and evaluate the outcome.} \item{mean.diff.in.CM}{Threshold of expression difference for selecting genes in the component merging strategy. We merge three-component to two-component by selecting genes with similar expressions for the two known components. Genes with the mean differences less than the threshold will be selected for component merging. It is used in the three-component setting, and is enabled when if.filter = TRUE. The default is 0.25.} \item{nspikein}{The number of spikes in normal reference used for proportion estimation. The default value is \eqn{ min(200, 0.3*My)}, where \eqn{My} the number of mixed samples. If it is set to 0, proportion estimation is performed without any spike in normal reference.} \item{tol}{The convergence criterion. The default is 10^(-5).} \item{pi01}{Initialized proportion for first kown component. The default is \eqn{Null} and pi01 will be generated randomly from uniform distribution.} \item{pi02}{Initialized proportion for second kown component. pi02 is needed only for running a three-component model. The default is \eqn{Null} and pi02 will be generated randomly from uniform distribution.} \item{nthread}{The number of threads used for deconvolution when OpenMP is available in the system. The default is the number of whole threads minus one. In our no-OpenMP version, it is set to 1.} } \value{ \item{pi}{A matrix of estimated proportion. First row and second row corresponds to the proportion estimate for the known components and unkown component respectively for two or three component settings, and each column corresponds to one sample.} \item{pi.iter}{Estimated proportions in each iteration. It is a \eqn{niter *My*p} array, where \eqn{p} is the number of components. This is enabled only when output.more.info = TRUE.} \item{gene.name}{The names of genes used in estimating the proportions. If no gene names are provided in the original data set, the genes will be automatically indexed.} } \description{ This function is designed to estimate the proportions of all mixed samples for each mixing component with a new proposed profile likelihood based gene selection, which can select most identifiable genes as reference gene sets to achieve better model fitting quality. We first calculated the Hessian matrix of the parameter spaces and then derive the confidence interval of the profile likelihood of each gene. We then utilized the length of confidence interval as a metric to rank the identifiability of genes. As a result, the proposed gene selection approach can improve the tumor-specific transcripts proportion estimation. } \note{ A Hessian matrix file will be created in the working directory and the corresponding Hessian matrix with an encoded name from the mixed tumor sample data will be saved under this file. If a user reruns this function with the same dataset, this Hessian matrix will be loaded to in place of running the profile likelihood method and reduce running time. } \examples{ # Example 1: estimate proportions for simulated two-component data # with spike-in normal reference data(test.data.2comp) # res.GS = DeMixT_GS(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 10, nbin = 50, nspikein = 50, # if.filter = TRUE, ngene.Profile.selected = 150, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) # # Example 2: estimate proportions for simulated two-component data # without spike-in normal reference # data(test.dtat.2comp) # res.GS = DeMixT_GS(data.Y = test.data.2comp$data.Y, # data.N1 = test.data.2comp$data.N1, # niter = 10, nbin = 50, nspikein = 0, # if.filter = TRUE, ngene.Profile.selected = 150, # mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150, # tol = 10^(-5)) } \references{ Gene Selection and Identifiability Analysis of RNA Deconvolution Models using Profile Likelihood. Manuscript in preparation. } \seealso{ http://bioinformatics.mdanderson.org/main/DeMixT } \author{ Shaolong Cao, Zeya Wang, Wenyi Wang } \keyword{DeMixT_GS} DeMixT/man/DeMixT_S2.Rd0000644000175200017520000000775614710233064015505 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DeMixT_S2.R \name{DeMixT_S2} \alias{DeMixT_S2} \title{Deconvolves expressions of each individual sample for unknown component} \usage{ DeMixT_S2( data.Y, data.N1, data.N2 = NULL, givenpi, nbin = 50, nthread = parallel::detectCores() - 1 ) } \arguments{ \item{data.Y}{A SummarizedExperiment object of expression data from mixed tumor samples. It is a \eqn{G} by \eqn{My} matrix where \eqn{G} is the number of genes and \eqn{My} is the number of mixed samples. Samples with the same tissue type should be placed together in columns.} \item{data.N1}{A SummarizedExperiment object of expression data from reference component 1 (e.g., normal). It is a \eqn{G} by \eqn{M1} matrix where \eqn{G} is the number of genes and \eqn{M1} is the number of samples for component 1.} \item{data.N2}{A SummarizedExperiment object of expression data from additional reference samples. It is a \eqn{G} by \eqn{M2} matrix where \eqn{G} is the number of genes and \eqn{M2} is the number of samples for component 2. Component 2 is needed only for running a three-component model.} \item{givenpi}{A vector of proportions for all mixed tumor samples. In two-component analysis, it gives the proportions of the unknown reference component, and in three-component analysis, it gives the proportions for the two known components.} \item{nbin}{Number of bins used in numerical integration for computing complete likelihood. A larger value increases accuracy in estimation but increases the running time, especially in a three-component deconvolution problem. The default is 50.} \item{nthread}{The number of threads used for deconvolution when OpenMP is available in the system. The default is the number of whole threads minus one. In our no-OpenMP version, it is set to 1.} } \value{ \item{decovExprT}{A matrix of deconvolved expression profiles corresponding to T-component in mixed samples for a given subset of genes. Each row corresponds to one gene and each column corresponds to one sample.} \item{decovExprN1}{A matrix of deconvolved expression profiles corresponding to N1-component in mixed samples for a given subset of genes. Each row corresponds to one gene and each column corresponds to one sample.} \item{decovExprN2}{A matrix of deconvolved expression profiles corresponding to N2-component in mixed samples for a given subset of genes in a three-component setting. Each row corresponds to one gene and each column corresponds to one sample.} \item{decovMu}{A matrix of estimated \eqn{Mu} of log2-normal distribution for both known (\eqn{MuN1, MuN2}) and unknown component (\eqn{MuT}). Each row corresponds to one gene.} \item{decovSigma}{Estimated \eqn{Sigma} of log2-normal distribution for both known (\eqn{SigmaN1, SigmaN2}) and unknown component (\eqn{SigmaT}). Each row corresponds to one gene.} } \description{ This function is designed to estimate the deconvolved expressions of individual mixed tumor samples for unknown component for each gene. } \examples{ # Example 1: two-component deconvolution given proportions data(test.data.2comp) givenpi <- c(t(as.matrix(test.data.2comp$pi[-2,]))) res.S2 <- DeMixT_S2(data.Y = test.data.2comp$data.Y, data.N1 = test.data.2comp$data.N1, data.N2 = NULL, givenpi = givenpi, nbin = 50) # # Example 2: three-component deconvolution given proportions # data(test.data.3comp) # givenpi = c(t(test.data.3comp$pi[-3,])) # res <- DeMixT_S2(data.Y = test.data.3comp$data.Y, # data.N1 = test.data.3comp$data.N1, # data.N2 = test.data.3comp$data.N2, # givenpi = givenpi, # nbin = 50) } \references{ Wang Z, Cao S, Morris J S, et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience, 2018, 9: 451-460. } \seealso{ http://bioinformatics.mdanderson.org/main/DeMixT } \author{ Zeya Wang, Wenyi Wang } \keyword{DeMixT_S2} DeMixT/man/DeMixT_preprocessing.Rd0000644000175200017520000000256214710233064020072 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DeMixT_Preprocessing.R \name{DeMixT_preprocessing} \alias{DeMixT_preprocessing} \title{DeMixT_preprocessing} \usage{ DeMixT_preprocessing( count.matrix, normal.id, tumor.id, cutoff_normal_range = c(0.1, 1), cutoff_tumor_range = c(0, 2.5), cutoff_step = 0.2 ) } \arguments{ \item{count.matrix}{A matrix of raw expression count with \eqn{G} by \eqn{(My + M1)}, where \eqn{G} is the number of genes, \eqn{My} is the number of mixed samples and \eqn{M1} is the number of normal samples. Row names are genes column names are sample ids.} \item{normal.id}{A vector of normal sample ids} \item{tumor.id}{A vector of tumor sample ids} \item{cutoff_normal_range}{A vector of two numeric values, indicating the lower and upper bounds of standard deviation of log2 count matrix from the normal samples to subset. Default is c(0.2, 0.6)} \item{cutoff_tumor_range}{A vector of two numeric values, indicating the lower and upper bounds to search standard deviation of log2 count matrix from the normal samples to subset. Default is c(0.2, 0.6)} \item{cutoff_step}{A scatter value indicating the step size of changing cutoff_normal_range and cutoff_tumor_range to find a suitable subset of count matrix for downstream analysis} } \value{ processed count matrix } \description{ DeMixT preprocessing in one go } DeMixT/man/Optimum_KernelC.rd0000644000175200017520000001457714710233064017103 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Optimum_KernelC.R \name{Optimum_KernelC} \alias{Optimum_KernelC} \title{Kernel function for optimizing parameters and hidden variables in DeMixT} \usage{ Optimum_KernelC( inputdata, groupid, nspikein, setting.pi, givenpi, givenpiT, niter, ninteg, tol, sg0 = 0.5^2, mu0 = 0, pi01 = NULL, pi02 = NULL, nthread = 1 ) } \arguments{ \item{inputdata}{A matrix of expression data (e.g gene expressions) from reference (e.g. normal) and mixed samples (e.g. mixed tumor samples). It is a \eqn{G*M} matrix where \eqn{G} is the number of genes and \eqn{M} is the number of samples including reference and mixed samples. Samples with the same tissue type should be placed together in columns (e.g. cbind(normal amples, mixed tumor samples).} \item{groupid}{A vector of indicators to denote if the corresponding samples are reference samples or mixed tumor samples. DeMixT is able to deconvolve mixed tumor samples with at most three components. We use 1 and 2 to denote the samples referencing the first and the second known component in mixed tumor samples. We use 3 to indicate mixed tumor samples prepared to be deconvolved. For example, in two-component deconvolution, we have c(1,1,...,3,3) and in three-component deconvolution, we have c(1,1,...,2,2,...,3,3).} \item{nspikein}{The number of spikes in normal reference used for proportion estimation. The default value is \eqn{ min(200, 0.3*My)}, where \eqn{My} the number of mixed tumor samples. If it is set to 0, proportion estimation is performed without any spike in normal reference.} \item{setting.pi}{If it is set to 0, then deconvolution is performed without any given proportions; if set to 1, deconvolution with given proportions for the first and the second known component is run; if set to 2, deconvolution is run with given tumor proportions. This option helps to perform deconvolution in different settings. In estimation of component-specific proportions, we use a subset of genes ; so when it is required to deconvolve another subset of genes, we just easily plug back our estimated proportions by setting this option to 1. In our two-step estimation strategy in a three-component setting, this option is set to 2 to implement the second step.} \item{givenpi}{\eqn{ST}-Vector of proportions. Given the number of mixed tumor samples is \eqn{My(My #include #include #include #include #include #include /* DBL_EPSILON */ #include "DeMixTH.h" #ifdef _OPENMP #include /* parallel computing */ #endif int checkopenmp(int* numthread) { #ifdef _OPENMP #pragma omp parallel #pragma omp master *numthread = omp_get_num_threads(); #else #endif return 0; } void Tdemix(double *data, int *nGroup, int *nsamp, int *ngenes, int *nspikein, int *npi, double *pi01, double *pi02, double *fixpi1, double *fixpi2, double *fixpi3,int *nCid, int *niter, int *ninteg, double *tol, int *thread, double *s0, double *m0, double *output1, double *output3, double *output5, double *output7, double *output9, double *output11, double *obj_out, double *output31, double *output32) { //nCid = 1, we have just one stroma component; =2, we have 2 int i, j, k, l; double *st1_mu, *st2_mu, *st1_sig, *st2_sig; double *st1_mu_2, *st2_mu_2, *st1_sig_2, *st2_sig_2; double **stroma1, **stroma2; double **stroma1_2, **stroma2_2; double total_tol; double **mixed; double *mixedm; #ifdef _OPENMP int nthread=*thread; #endif //clock_t start_t, end_t; //initial value for sigma and mu double ss0 = *s0; double mm0 = *m0; //set the number of threads #ifdef _OPENMP omp_set_num_threads(nthread); #endif //get value from input parameter nS=*nsamp; // Number of Samples nG=*ngenes; // Number of Genes nSp=*nspikein; // Numer of Spike in Samples nHavepi = *npi; // Have pi or not: 0: Not given pi1 and pi2; 1: given pi1 and pi2; 2: given piT Cid = *nCid; //indicator for number of components integ = *ninteg; total_tol = *tol; iteration = *niter; // number of iterations //Rprintf("iteration is %d\n", iteration); // 1 for component 1, 2 for component 2, 3 for mixed sample fNorm1=0; fNorm2=0; for(j=0;jNavg1[j]=st1_mu[j]; if(Cid == 2) p->Navg2[j]=st2_mu[j]; p->Nsigma1[j]= pow(st1_sig[j], 2.0); if(Cid == 2) p->Nsigma2[j]= pow(st2_sig[j], 2.0); //p->Tsigma[j]=0.25; p->Tsigma[j] = ss0; if(Cid == 1){ Tavgtmp = 2*mixedm[j] - exp(p->Navg1[j]*log(2.0) + p->Nsigma1[j]/2.0*pow(log(2.0), 2.0)); }else if(Cid == 2){ Tavgtmp = 3*mixedm[j] - exp(p->Navg1[j]*log(2.0) + p->Nsigma1[j]/2.0*pow(log(2.0), 2.0)) - exp(p->Navg2[j]*log(2.0) + p->Nsigma2[j]/2.0*pow(log(2.0), 2.0)); } if(Tavgtmp <= 0) Tavgtmp = exp(10*log(2.0) + p->Tsigma[j]/2.0*pow(log(2.0), 2.0)); p->Tavg[j] = (log(Tavgtmp) - p->Tsigma[j]/2.0*pow(log(2.0), 2.0))/log(2.0); if(mm0 > 0.0) p->Tavg[j] = mm0; //p->Tavg[j] = 10.0; //Rprintf("gene avg for %d is %lf\n", j, p->Tavg[j]); } //Rprintf("Initial pi0 \n"); for(k=0;kpi1[k]= pi01[k]; //Rprintf("%15.3f \t", p->pi1[k], '\n'); if(Cid == 2) p->pi2[k]= pi02[k]; } if(nHavepi==1) { for(k=0;kpi1[k]= fixpi1[k]; if(Cid == 2) p->pi2[k]= fixpi2[k]; } }else if(nHavepi == 2){ for(k=0;kpiT[k] = fixpi3[k]; } //Rprintf("Loading is over\n"); free(st1_mu); free(st2_mu); free(st1_sig); free(st2_sig); //begin our iteration // for(j=0;jNsigma1[j]); // Rprintf("nsigma2 for %d is %lf\n",j, p->Nsigma2[j]); // Rprintf("navg1 for %d is %lf\n",j, p->Navg1[j]); // Rprintf("navg2 for %d is %lf\n",j, p->Navg2[j]); // } CD = calloc(intx ,sizeof(double *)); for(j=0;jobj[k] = 0; } iteration1=0; double obj_old_all=0.0; for(i=0;iTavg[j]; sigparT[i][j] = p->Tsigma[j]; } #else for(j=0;jTavg[j]; sigparT[i][j] = p->Tsigma[j]; } #endif if (nHavepi != 1) Rprintf("Iteration %d: updating proportions\n", i+1); //updating pi value if(nHavepi==0) { #ifdef _OPENMP // multithreaded OpenMP version of code #pragma omp parallel for //openmp for(l=0;lpi1[j]; if (nHavepi != 1) {}; //Rprintf("%15.3f \t", p->pi1[j]); if(Cid == 2) { tmppi2[i][j] = p->pi2[j]; if (nHavepi != 1){}; //Rprintf(" %15.3f \t", p->pi2[j]); } if (nHavepi != 1) {}; //Rprintf("\n"); } // Total time expectation depending on the first five iterations if(i==1) { //end_t=clock(); //Rprintf("================================================================\n"); //Rprintf("Deconvolution is estimated to be finished in at most %lf hours\n", (double)(end_t-start_t)/CLOCKS_PER_SEC*((double)iteration/3600.0)); //Rprintf("================================================================\n"); } // objective function 2D double ttmp=0.0, tttmp; for(j=0;jNavg1[j], p->Navg2[j], p->Tavg[j], p->Nsigma1[j], p->Nsigma2[j], p->Tsigma[j], p->pi1[l], p->pi2[l]); ttmp = ttmp - tttmp; }else{ tttmp = ft_y(FD[l+fNorm][j], p->Navg1[j], p->Tavg[j], p->Nsigma1[j], p->Tsigma[j], p->pi1[l], p->pi2[l]); ttmp = ttmp - tttmp; //Rprintf("obj teset is %f, %d\n", tttmp, j); //Rprintf("obj val is %f\t%f\t%f\t %f\t%f\t%f\t%f\n",FD[l+fNorm][j], p->Navg1[j], p->Tavg[j], p->Nsigma1[j], p->Tsigma[j], p->pi1[l], p->pi2[l]); } } } if(i>0){ obj_old_all=p->obj[i-1]; } p->obj[i] = ttmp; //Rprintf("obj teset is %f\n", ttmp); iteration1++; //Rprintf("Obj error is %f\n", fabs(ttmp - obj_old_all)); if(fabs(ttmp - obj_old_all)mle[j][k]; //deconvolved value } } fflush(NULL); saveFiles(output1, output3, output5, output7, output9, output11, obj_out, output31, output32); //Rprintf("Step 4: Save files done : \n"); //free variable free(p->Navg1); free(p->Navg2); free(p->Tavg); free(p->Nsigma1); free(p->Nsigma2); free(p->Tsigma); free(p->pi1); free(p->pi2); free(p->obj); for(i=0;imle[i]); } free(p->mle); free(p); } /************************************************************************************ * Function to load data and save data ************************************************************************************/ // Data transfer void load_data(double *mat1) { int j,k; for(k=0;kNavg1 =calloc(nG ,sizeof(double )); qq->Navg2 =calloc(nG ,sizeof(double )); qq->Tavg =calloc(nG ,sizeof(double )); qq->Nsigma1 =calloc(nG ,sizeof(double )); qq->Nsigma2 =calloc(nG ,sizeof(double )); qq->Tsigma = calloc(nG ,sizeof(double )); qq->pi1 =calloc(intx ,sizeof(double )); qq->pi2 =calloc(intx ,sizeof(double )); qq->piT = calloc(intx ,sizeof(double )); qq->mle =calloc(nG ,sizeof(double *)); qq->obj =calloc(iteration ,sizeof(double )); for(i=0;imle[i]=calloc(nmle, sizeof(double )); } // Deconvolved expressions void saveFiles(double *put1, double *put3, double *put5, double *put7, double *put9, double *put11, double *obj_out, double *put31, double *put32) { int i, j; //pi1 and pi2 save if(Cid == 2) //tow components { for(i=0;ipi1[i]; put1[i+intx*1] = p->pi2[i]; } }else{ for(i=0;ipi1[i]; } // pure tumor expression save. for(j=0;jmle[j][i+intx]; if(Cid == 2) put32[j*intx+i] = p->mle[j][i+2*intx]; } } //mu and sigma save for T for(j=0;jobj[i]; } } /************************************************************************************ * get tumor function *************************************************************************************/ /*Tumor Component Update*/ void gettumor(int genes, int h) // option h = 1 for one component; h = 2 for two { double mu, sigma,upp,low,upps; upp = 33; if(nHavepi == 1) { upps = 100; }else{ upps = 25; } low = 0; double obj_old, obj_new; if(h == 1) { obj_old = mint(genes, h, p->Tavg[genes]); mu = tmin_y(low, upp, genes, h, mint, 0.001); obj_new = mint(genes, h, mu); //if((obj_new < obj_old) || iteration1 == 0) p->Tavg[genes] = mu; obj_old = tf_y(genes, p->Tavg[genes], p->Tsigma[genes]); sigma = tmin_y2(0.0001, upps, genes, p->Tavg[genes], tf_y, 0.0001); obj_new = tf_y(genes, p->Tavg[genes], sigma); //if((obj_new < obj_old) || iteration1 == 0) p->Tsigma[genes] = sigma; //Rprintf("obj_old is %lf %d\t", obj_old, genes); //Rprintf("obj_new is %lf %d\t", obj_new, genes); }else{ obj_old = mint(genes, h, p->Tavg[genes]); mu = tmin_y(low, upp, genes,h, mint, 0.001); obj_new = mint(genes, h, mu); //if((obj_new < obj_old) || iteration1 == 0) p->Tavg[genes] = mu; obj_old = tf_y2(genes, p->Tavg[genes], p->Tsigma[genes]); sigma = tmin_y2(0.0001, upps, genes, p->Tavg[genes], tf_y2, 0.0001); obj_new = tf_y2(genes, p->Tavg[genes], sigma); //if((obj_new < obj_old) || iteration1 == 0) p->Tsigma[genes] = sigma; } } double tf_y(int genes, double mu, double sigma) { double new_like; double tmp; int i; new_like=0.0; for (i=0; iNavg1[genes], mu, p->Nsigma1[genes], sigma, p->pi1[i], 0.0); new_like+= tmp; } return (new_like*(-1.0)); } double tf_y2(int genes, double mu, double sigma) { double new_like; double tmp; int i; new_like=0.0; for (i=0; iNavg1[genes], p->Navg2[genes], mu, p->Nsigma1[genes], p->Nsigma2[genes], sigma, p->pi1[i], p->pi2[i]); new_like+= tmp; } return (new_like*(-1.0)); } double mint(int genes, int h, double mu) // opt h =1:one component; h =2:two component { double sigma; double tmp,tmp0; double upps; if(nHavepi == 1) { upps = 100; }else{ upps = 25; } if(h == 1) { sigma = tmin_y2(0.0001, upps, genes, mu, tf_y, 0.0001); tmp = tf_y(genes, mu, sigma); tmp0 = tf_y(genes, mu, p->Tsigma[genes]); if(tmp0 < tmp) { sigma = p->Tsigma[genes]; tmp = tmp0; } }else{ sigma = tmin_y2(0.0001, upps, genes, mu, tf_y2, 0.0001); tmp = tf_y2(genes, mu, sigma); tmp0 = tf_y2(genes, mu, p->Tsigma[genes]); if(tmp0 < tmp) { sigma = p->Tsigma[genes]; tmp = tmp0; } } return (tmp); } double tmin_y(double ax, double bx, int genes, int h, double (*f)(int, int, double), double tol) { /* c is the squared inverse of the golden ratio */ const double c = (3. - sqrt(5.)) * .5; /* Local variables */ double a, b, d, e, p, q, r, u, v, w, x; double t2, fu, fv, fw, fx, xm, eps, tol1, tol3; /* eps is approximately the square root of the relative machine precision. */ eps = DBL_EPSILON; tol1 = eps + 1.;/* the smallest 1.000... > 1 */ eps = sqrt(eps); a = ax; b = bx; v = a + c * (b - a); w = v; x = v; d = 0.;/* -Wall */ e = 0.; fx = (*f)(genes, h, x); fv = fx; fw = fx; tol3 = tol / 3.; /* main loop starts here ----------------------------------- */ for(;;) { xm = (a + b) * .5; tol1 = eps * fabs(x) + tol3; t2 = tol1 * 2.; /* check stopping criterion */ if (fabs(x - xm) <= t2 - (b - a) * .5) break; p = 0.; q = 0.; r = 0.; if (fabs(e) > tol1) { /* fit parabola */ r = (x - w) * (fx - fv); q = (x - v) * (fx - fw); p = (x - v) * q - (x - w) * r; q = (q - r) * 2.; if (q > 0.) p = -p; else q = -q; r = e; e = d; } if (fabs(p) >= fabs(q * .5 * r) || p <= q * (a - x) || p >= q * (b - x)) { /* a golden-section step */ if (x < xm) e = b - x; else e = a - x; d = c * e; } else { /* a parabolic-interpolation step */ d = p / q; u = x + d; /* f must not be evaluated too close to ax or bx */ if (u - a < t2 || b - u < t2) { d = tol1; if (x >= xm) d = -d; } } /* f must not be evaluated too close to x */ if (fabs(d) >= tol1) u = x + d; else if (d > 0.) u = x + tol1; else u = x - tol1; fu = (*f)(genes, h, u); /* update a, b, v, w, and x */ if (fu <= fx) { if (u < x) b = x; else a = x; v = w; w = x; x = u; fv = fw; fw = fx; fx = fu; } else { if (u < x) a = u; else b = u; if (fu <= fw || w == x) { v = w; fv = fw; w = u; fw = fu; } else if (fu <= fv || v == x || v == w) { v = u; fv = fu; } } } /* end of main loop */ return x; } double tmin_y2(double ax, double bx, int genes, double mu, double (*f)(int, double, double), double tol) { /* c is the squared inverse of the golden ratio */ const double c = (3. - sqrt(5.)) * .5; /* Local variables */ double a, b, d, e, p, q, r, u, v, w, x; double t2, fu, fv, fw, fx, xm, eps, tol1, tol3; /* eps is approximately the square root of the relative machine precision. */ eps = DBL_EPSILON; tol1 = eps + 1.;/* the smallest 1.000... > 1 */ eps = sqrt(eps); a = ax; b = bx; v = a + c * (b - a); w = v; x = v; d = 0.;/* -Wall */ e = 0.; fx = (*f)(genes, mu, x); fv = fx; fw = fx; tol3 = tol / 3.; /* main loop starts here ----------------------------------- */ for(;;) { xm = (a + b) * .5; tol1 = eps * fabs(x) + tol3; t2 = tol1 * 2.; /* check stopping criterion */ if (fabs(x - xm) <= t2 - (b - a) * .5) break; p = 0.; q = 0.; r = 0.; if (fabs(e) > tol1) { /* fit parabola */ r = (x - w) * (fx - fv); q = (x - v) * (fx - fw); p = (x - v) * q - (x - w) * r; q = (q - r) * 2.; if (q > 0.) p = -p; else q = -q; r = e; e = d; } if (fabs(p) >= fabs(q * .5 * r) || p <= q * (a - x) || p >= q * (b - x)) { /* a golden-section step */ if (x < xm) e = b - x; else e = a - x; d = c * e; } else { /* a parabolic-interpolation step */ d = p / q; u = x + d; /* f must not be evaluated too close to ax or bx */ if (u - a < t2 || b - u < t2) { d = tol1; if (x >= xm) d = -d; } } /* f must not be evaluated too close to x */ if (fabs(d) >= tol1) u = x + d; else if (d > 0.) u = x + tol1; else u = x - tol1; fu = (*f)(genes, mu, u); /* update a, b, v, w, and x */ if (fu <= fx) { if (u < x) b = x; else a = x; v = w; w = x; x = u; fv = fw; fw = fx; fx = fu; } else { if (u < x) a = u; else b = u; if (fu <= fw || w == x) { v = w; fv = fw; w = u; fw = fu; } else if (fu <= fv || v == x || v == w) { v = u; fv = fu; } } } /* end of main loop */ return x; } /************************************************************************************* *a function to search the mode for better numerical integration *************************************************************************************/ double nitg_ft_y(double t, double y, double mung, double mutg, double sng, double stg, double pi1){ double tmp; tmp = -0.5*log(sng)-0.5*log(stg) -log(y-t)-log(t); tmp = tmp-0.5*pow(log2(t)-(log2(pi1) + mung), 2.0) /sng; tmp = tmp-0.5*pow(log2(y-t)-(log2(1-pi1) + mutg), 2.0)/stg; tmp = exp(tmp); return -tmp; } double min_nitg_ft_y(double ax, double bx, double y, double mung, double mutg, double sng, double stg, double pi1, double (*f)(double, double, double, double, double, double,double), double tol) { /* c is the squared inverse of the golden ratio */ const double c = (3. - sqrt(5.)) * .5; /* Local variables */ double a, b, d, e, p, q, r, u, v, w, x; double t2, fu, fv, fw, fx, xm, eps, tol1, tol3; /* eps is approximately the square root of the relative machine precision. */ eps = DBL_EPSILON; tol1 = eps + 1.;/* the smallest 1.000... > 1 */ eps = sqrt(eps); a = ax; b = bx; v = a + c * (b - a); w = v; x = v; d = 0.;/* -Wall */ e = 0.; fx = (*f)(x, y, mung, mutg, sng, stg, pi1); fv = fx; fw = fx; tol3 = tol / 3.; /* main loop starts here ----------------------------------- */ for(;;) { xm = (a + b) * .5; tol1 = eps * fabs(x) + tol3; t2 = tol1 * 2.; /* check stopping criterion */ if (fabs(x - xm) <= t2 - (b - a) * .5) break; p = 0.; q = 0.; r = 0.; if (fabs(e) > tol1) { /* fit parabola */ r = (x - w) * (fx - fv); q = (x - v) * (fx - fw); p = (x - v) * q - (x - w) * r; q = (q - r) * 2.; if (q > 0.) p = -p; else q = -q; r = e; e = d; } if (fabs(p) >= fabs(q * .5 * r) || p <= q * (a - x) || p >= q * (b - x)) { /* a golden-section step */ if (x < xm) e = b - x; else e = a - x; d = c * e; } else { /* a parabolic-interpolation step */ d = p / q; u = x + d; /* f must not be evaluated too close to ax or bx */ if (u - a < t2 || b - u < t2) { d = tol1; if (x >= xm) d = -d; } } /* f must not be evaluated too close to x */ if (fabs(d) >= tol1) u = x + d; else if (d > 0.) u = x + tol1; else u = x - tol1; fu = (*f)(u, y, mung, mutg, sng, stg, pi1); /* update a, b, v, w, and x */ if (fu <= fx) { if (u < x) b = x; else a = x; v = w; w = x; x = u; fv = fw; fw = fx; fx = fu; } else { if (u < x) a = u; else b = u; if (fu <= fw || w == x) { v = w; fv = fw; w = u; fw = fu; } else if (fu <= fv || v == x || v == w) { v = u; fv = fu; } } } /* end of main loop */ return x; } /************************************************************************************ * getpi function *************************************************************************************/ /*optimize pi for updating*/ //for calculating pi1 /* For update ratio calculation, we need to fix position at the same number*/ double ft_y(double y, double mung, double mutg, double sng, double stg, double pi1, double pi2) { int i; double tmode; double ltmode, rtmode; double rtval=0.0; double tmp; double tmp_pos[integ]; double cutd; int integ1, integ2; double rtval1=0.0, rtval2=0.0; integ1 = integ/6; //small bin size integ2 = integ - integ1; //large bin size double tmp_pos1[integ1], tmp_pos2[integ2]; //this step is for finding the mode to strengthen numerical integration if(fabs(mutg - mung) > 4.0){ tmode = min_nitg_ft_y(0.1, y-0.1, y, mung, mutg, sng, stg, pi1, nitg_ft_y, 0.01); ltmode = tmode; rtmode = y - tmode; //we cut the interval to two segmentations if(ltmode - rtmode > 0){ cutd = max(y/6, tmode - 1.5*(y - tmode)); for(i=0;iNavg1[i], p->Tavg[i], p->Nsigma1[i], p->Tsigma[i], pi1, 0); new_like+= tmp; } return (new_like*(-1.0)); } double pmin_y(double ax, double bx, int samp, double (*f)(int, double), double tol) { /* c is the squared inverse of the golden ratio */ const double c = (3. - sqrt(5.)) * .5; /* Local variables */ double a, b, d, e, p, q, r, u, v, w, x; double t2, fu, fv, fw, fx, xm, eps, tol1, tol3; /* eps is approximately the square root of the relative machine precision. */ eps = DBL_EPSILON; tol1 = eps + 1.;/* the smallest 1.000... > 1 */ eps = sqrt(eps); a = ax; b = bx; v = a + c * (b - a); w = v; x = v; d = 0.;/* -Wall */ e = 0.; fx = (*f)(samp, x); fv = fx; fw = fx; tol3 = tol / 3.; /* main loop starts here ----------------------------------- */ for(;;) { xm = (a + b) * .5; tol1 = eps * fabs(x) + tol3; t2 = tol1 * 2.; /* check stopping criterion */ if (fabs(x - xm) <= t2 - (b - a) * .5) break; p = 0.; q = 0.; r = 0.; if (fabs(e) > tol1) { /* fit parabola */ r = (x - w) * (fx - fv); q = (x - v) * (fx - fw); p = (x - v) * q - (x - w) * r; q = (q - r) * 2.; if (q > 0.) p = -p; else q = -q; r = e; e = d; } if (fabs(p) >= fabs(q * .5 * r) || p <= q * (a - x) || p >= q * (b - x)) { /* a golden-section step */ if (x < xm) e = b - x; else e = a - x; d = c * e; } else { /* a parabolic-interpolation step */ d = p / q; u = x + d; /* f must not be evaluated too close to ax or bx */ if (u - a < t2 || b - u < t2) { d = tol1; if (x >= xm) d = -d; } } /* f must not be evaluated too close to x */ if (fabs(d) >= tol1) u = x + d; else if (d > 0.) u = x + tol1; else u = x - tol1; fu = (*f)(samp, u); /* update a, b, v, w, and x */ if (fu <= fx) { if (u < x) b = x; else a = x; v = w; w = x; x = u; fv = fw; fw = fx; fx = fu; } else { if (u < x) a = u; else b = u; if (fu <= fw || w == x) { v = w; fv = fw; w = u; fw = fu; } else if (fu <= fv || v == x || v == w) { v = u; fv = fu; } } } /* end of main loop */ return x; } //another function to get pi void getpi(int samp, int h) // option h = 1 for 1 component, 2 for two component { double pii1, pii2; double upp; pii1 = 0.0; pii2= 0.0; double obj_old, obj_new; if(h == 1) { obj_old = pf_y(samp, p->pi1[samp]); pii1 = pmin_y(0.01, 0.99, samp, pf_y, 0.0001); obj_new = pf_y(samp, pii1); //if((obj_new < obj_old) || iteration1 == 0) p->pi1[samp] = pii1; }else{ //two component obj_old = pf_y2(samp, p->pi1[samp], p->pi2[samp]); pii2 = pmin_y(0.01, 0.99, samp, minpi, 0.0001); upp = 1 - pii2; pii1 = pmin_y2(0.01, upp, samp, pii2, pf_y2, 0.0001); obj_new = pf_y2(samp, pii1, pii2); //if((obj_new < obj_old) || iteration1 == 0){ p->pi1[samp] = pii1; p->pi2[samp] = pii2; //} } } // another function to get spike in pi void getspikeinpi(int samp) { double pii1; double obj_old, obj_new; obj_old = pf_y(samp, p->pi1[samp]); pii1 = 0.99; obj_new = pf_y(samp, pii1); p->pi1[samp] = pii1; } //another function to get pi given piT void getpiT(int samp) // for two component given piT { double pii1, pii2, piiT; double upp; pii1 = 0.0; pii2= 0.0; double obj_old, obj_new; piiT = p->piT[samp]; obj_old = pf_yT(samp, p->pi1[samp], piiT); //two component upp = 1 - piiT; pii1 = pmin_y2(0.01, upp, samp, piiT, pf_yT, 0.0001); pii2 = 1 - piiT - pii1; obj_new = pf_yT(samp, pii1, piiT); //if((obj_new < obj_old) || iteration1 == 0){ p->pi1[samp] = pii1; p->pi2[samp] = pii2; //} } // outer integration double ft_y2(double y, double mung1, double mung2, double mutg, double sng1, double sng2, double stg, double pi1, double pi2) { int i; double rtval; double tmp; double res; double tmp_pos[integ]; rtval=0; for(i=0;iNavg1[i], p->Navg2[i], p->Tavg[i], p->Nsigma1[i], p->Nsigma2[i], p->Tsigma[i], pi1, pi2); new_like+= tmp; } return (new_like*(-1.0)); } //the joint likelihood function given piiT double pf_yT(int samp, double pi1, double piT) { double new_like; double tmp; double pi2; int i; new_like=0.0; pi2 = 1 - piT -pi1; for (i=0; iNavg1[i], p->Navg2[i], p->Tavg[i], p->Nsigma1[i], p->Nsigma2[i], p->Tsigma[i], pi1, pi2); new_like+= tmp; } return (new_like*(-1.0)); } double pmin_y2(double ax, double bx, int samp, double pi2, double (*f)(int, double, double), double tol) { /* c is the squared inverse of the golden ratio */ const double c = (3. - sqrt(5.)) * .5; /* Local variables */ double a, b, d, e, p, q, r, u, v, w, x; double t2, fu, fv, fw, fx, xm, eps, tol1, tol3; /* eps is approximately the square root of the relative machine precision. */ eps = DBL_EPSILON; tol1 = eps + 1.;/* the smallest 1.000... > 1 */ eps = sqrt(eps); a = ax; b = bx; v = a + c * (b - a); w = v; x = v; d = 0.;/* -Wall */ e = 0.; fx = (*f)(samp, x, pi2); fv = fx; fw = fx; tol3 = tol / 3.; /* main loop starts here ----------------------------------- */ for(;;) { xm = (a + b) * .5; tol1 = eps * fabs(x) + tol3; t2 = tol1 * 2.; /* check stopping criterion */ if (fabs(x - xm) <= t2 - (b - a) * .5) break; p = 0.; q = 0.; r = 0.; if (fabs(e) > tol1) { /* fit parabola */ r = (x - w) * (fx - fv); q = (x - v) * (fx - fw); p = (x - v) * q - (x - w) * r; q = (q - r) * 2.; if (q > 0.) p = -p; else q = -q; r = e; e = d; } if (fabs(p) >= fabs(q * .5 * r) || p <= q * (a - x) || p >= q * (b - x)) { /* a golden-section step */ if (x < xm) e = b - x; else e = a - x; d = c * e; } else { /* a parabolic-interpolation step */ d = p / q; u = x + d; /* f must not be evaluated too close to ax or bx */ if (u - a < t2 || b - u < t2) { d = tol1; if (x >= xm) d = -d; } } /* f must not be evaluated too close to x */ if (fabs(d) >= tol1) u = x + d; else if (d > 0.) u = x + tol1; else u = x - tol1; fu = (*f)(samp, u, pi2); /* update a, b, v, w, and x */ if (fu <= fx) { if (u < x) b = x; else a = x; v = w; w = x; x = u; fv = fw; fw = fx; fx = fu; } else { if (u < x) a = u; else b = u; if (fu <= fw || w == x) { v = w; fv = fw; w = u; fw = fu; } else if (fu <= fv || v == x || v == w) { v = u; fv = fu; } } } /* end of main loop */ return x; } //get pi1 function value given pi2 double minpi(int samp, double pi2) { double pi1; double tmp; double upp = 1 - pi2; pi1 = pmin_y2(0.01, upp, samp, pi2, pf_y2, 0.0001); tmp = pf_y2(samp, pi1, pi2); return (tmp); } /************************************************************************************ *mle optimization function ************************************************************************************/ //optimize function void getmle(int opt, int opt2, int h) { double tmp, tmp1, tmp2; double upp; double y_sum, pi_sum; if(h == 1) //one component { upp = (FD[opt+fNorm][opt2] - 1 + p->pi1[opt])/p->pi1[opt]; y_sum = FD[opt+fNorm][opt2]; tmp = fmin1(1, upp, opt2, opt, y_sum, 1, lf1, 0.0001); p->mle[opt2][opt+intx] = tmp; p->mle[opt2][opt] = (FD[opt+fNorm][opt2] - p->pi1[opt]*tmp )/(1 - p->pi1[opt]); }else{ upp = (FD[opt+fNorm][opt2] - 1 + p->pi1[opt] + p->pi2[opt])/p->pi2[opt]; tmp2 = fmin2(1, upp, opt2, opt, lf2, 0.0001); p->mle[opt2][opt+2*intx] = tmp2; upp = (FD[opt+fNorm][opt2] - 1 + p->pi1[opt] + p->pi2[opt] - p->pi2[opt]*tmp2)/p->pi1[opt]; y_sum = FD[opt+fNorm][opt2] - tmp2*p->pi2[opt]; pi_sum = 1 - p->pi2[opt]; tmp1 = fmin1(1, upp, opt2, opt, y_sum, pi_sum, lf1, 0.0001); p->mle[opt2][opt+intx] = tmp1; p->mle[opt2][opt] = (FD[opt+fNorm][opt2] - p->pi1[opt]*tmp1 - p->pi2[opt]*tmp2)/(1 - p->pi1[opt] - p->pi2[opt]); } } // kernel optimize function for just component 1 normal and tumor double lf1(int opt, int opt2, double y_sum, double pi_sum, double nval1) { double new_like; double tmp; new_like=0.0; new_like = -log(nval1) - 0.5*log(p->Nsigma1[opt2]) - 0.5*(log2(nval1) - p->Navg1[opt2])*(log2(nval1) - p->Navg1[opt2])/p->Nsigma1[opt2]; tmp= (y_sum - p->pi1[opt]*nval1)/(pi_sum - p->pi1[opt]); new_like += -log(tmp) -0.5*log(p->Tsigma[opt2]) - 0.5*(log2(tmp) - p->Tavg[opt2])*(log2(tmp) - p->Tavg[opt2])/p->Tsigma[opt2]; return (new_like*(-1.0)); } double fmin1(double ax, double bx,int iG, int iS, double y_sum, double pi_sum, double (*f)(int, int, double, double, double), double tol) { /* c is the squared inverse of the golden ratio */ const double c = (3. - sqrt(5.)) * .5; /* Local variables */ double a, b, d, e, p, q, r, u, v, w, x; double t2, fu, fv, fw, fx, xm, eps, tol1, tol3; /* eps is approximately the square root of the relative machine precision. */ eps = DBL_EPSILON; tol1 = eps + 1.;/* the smallest 1.000... > 1 */ eps = sqrt(eps); a = ax; b = bx; v = a + c * (b - a); w = v; x = v; d = 0.;/* -Wall */ e = 0.; fx = (*f)(iS, iG, y_sum, pi_sum, x); //Rprintf("optimized parameters are iS: %d, iG: %d, y_sum: %lf, pi_sum: %lf, x: %lf, fx:%lf \n",iS, iG, y_sum, pi_sum, x, fx); fv = fx; fw = fx; tol3 = tol / 3.; /* main loop starts here ----------------------------------- */ for(;;) { xm = (a + b) * .5; tol1 = eps * fabs(x) + tol3; t2 = tol1 * 2.; /* check stopping criterion */ if (fabs(x - xm) <= t2 - (b - a) * .5) break; p = 0.; q = 0.; r = 0.; if (fabs(e) > tol1) { /* fit parabola */ r = (x - w) * (fx - fv); q = (x - v) * (fx - fw); p = (x - v) * q - (x - w) * r; q = (q - r) * 2.; if (q > 0.) p = -p; else q = -q; r = e; e = d; } if (fabs(p) >= fabs(q * .5 * r) || p <= q * (a - x) || p >= q * (b - x)) { /* a golden-section step */ if (x < xm) e = b - x; else e = a - x; d = c * e; } else { /* a parabolic-interpolation step */ d = p / q; u = x + d; /* f must not be evaluated too close to ax or bx */ if (u - a < t2 || b - u < t2) { d = tol1; if (x >= xm) d = -d; } } /* f must not be evaluated too close to x */ if (fabs(d) >= tol1) u = x + d; else if (d > 0.) u = x + tol1; else u = x - tol1; fu = (*f)(iS, iG, y_sum, pi_sum, u); //Rprintf("optimized parameters are iS: %d, iG: %d, y_sum: %lf, pi_sum: %lf, x: %lf, fx:%lf \n",iS, iG, y_sum, pi_sum, x, fu); /* update a, b, v, w, and x */ if (fu <= fx) { if (u < x) b = x; else a = x; v = w; w = x; x = u; fv = fw; fw = fx; fx = fu; } else { if (u < x) a = u; else b = u; if (fu <= fw || w == x) { v = w; fv = fw; w = u; fw = fu; } else if (fu <= fv || v == x || v == w) { v = u; fv = fu; } } } /* end of main loop */ return x; } // wrapped optimize function for component 2 double lf2(int opt, int opt2, double nval2) { double tmp; double upp; double y_sum, pi_sum; double nval1; double new_like; new_like=0.0; upp = (FD[opt+fNorm][opt2] - 1 + p->pi1[opt] + p->pi2[opt] - p->pi2[opt]*nval2)/p->pi1[opt]; y_sum = FD[opt+fNorm][opt2] - nval2*p->pi2[opt]; pi_sum = 1 - p->pi2[opt]; nval1 = fmin1(1, upp, opt2, opt, y_sum, pi_sum, lf1, 0.0001); new_like = -log(nval1) - 0.5*log(p->Nsigma1[opt2]) - 0.5*(log2(nval1) - p->Navg1[opt2])*(log2(nval1) - p->Navg1[opt2])/p->Nsigma1[opt2]; new_like += -log(nval2) - 0.5*log(p->Nsigma2[opt2]) - 0.5*(log2(nval2) - p->Navg2[opt2])*(log2(nval2) - p->Navg2[opt2])/p->Nsigma2[opt2]; tmp= (FD[opt+fNorm][opt2] - p->pi1[opt]*nval1 - p->pi2[opt]*nval2)/(1 - p->pi1[opt] - p->pi2[opt]); new_like += -log(tmp) -0.5*log(p->Tsigma[opt2]) - 0.5*(log2(tmp) - p->Tavg[opt2])*(log2(tmp) - p->Tavg[opt2])/p->Tsigma[opt2]; return (new_like*(-1.0)); } double fmin2(double ax, double bx,int iG, int iS, double (*f)(int, int, double), double tol) { /* c is the squared inverse of the golden ratio */ const double c = (3. - sqrt(5.)) * .5; /* Local variables */ double a, b, d, e, p, q, r, u, v, w, x; double t2, fu, fv, fw, fx, xm, eps, tol1, tol3; /* eps is approximately the square root of the relative machine precision. */ eps = DBL_EPSILON; tol1 = eps + 1.;/* the smallest 1.000... > 1 */ eps = sqrt(eps); a = ax; b = bx; v = a + c * (b - a); w = v; x = v; d = 0.;/* -Wall */ e = 0.; fx = (*f)(iS, iG, x); fv = fx; fw = fx; tol3 = tol / 3.; /* main loop starts here ----------------------------------- */ for(;;) { xm = (a + b) * .5; tol1 = eps * fabs(x) + tol3; t2 = tol1 * 2.; /* check stopping criterion */ if (fabs(x - xm) <= t2 - (b - a) * .5) break; p = 0.; q = 0.; r = 0.; if (fabs(e) > tol1) { /* fit parabola */ r = (x - w) * (fx - fv); q = (x - v) * (fx - fw); p = (x - v) * q - (x - w) * r; q = (q - r) * 2.; if (q > 0.) p = -p; else q = -q; r = e; e = d; } if (fabs(p) >= fabs(q * .5 * r) || p <= q * (a - x) || p >= q * (b - x)) { /* a golden-section step */ if (x < xm) e = b - x; else e = a - x; d = c * e; } else { /* a parabolic-interpolation step */ d = p / q; u = x + d; /* f must not be evaluated too close to ax or bx */ if (u - a < t2 || b - u < t2) { d = tol1; if (x >= xm) d = -d; } } /* f must not be evaluated too close to x */ if (fabs(d) >= tol1) u = x + d; else if (d > 0.) u = x + tol1; else u = x - tol1; fu = (*f)(iS, iG, u); /* update a, b, v, w, and x */ if (fu <= fx) { if (u < x) b = x; else a = x; v = w; w = x; x = u; fv = fw; fw = fx; fx = fu; } else { if (u < x) a = u; else b = u; if (fu <= fw || w == x) { v = w; fv = fw; w = u; fw = fu; } else if (fu <= fv || v == x || v == w) { v = u; fv = fu; } } } /* end of main loop */ return x; } DeMixT/src/DeMixTH.h0000644000175200017520000001447414710233064015142 0ustar00biocbuildbiocbuild #ifndef Zeya_Wang #define Zeya_Wang #define max(a,b) \ ({ __typeof__ (a) _a = (a); \ __typeof__ (b) _b = (b); \ _a > _b ? _a : _b; }) #define min(a,b) \ ({__typeof__ (a) _a = (a); \ __typeof__ (b) _b = (b); \ _a < _b ? _a : _b;}) // parameter definition typedef struct param{ //average parameter double *Navg1; double *Navg2; double *Tavg; //variance parameter double *Nsigma1; double *Nsigma2; double *Tsigma; //pi double *pi1; double *pi2; double *piT; //mle double **mle; double *obj; } PARAM; // Function initialize void initialSet(PARAM *qq); void load_data(double *mat1); void saveFiles(double *put1, double *put3, double *put5, double *put7, double *put9, double *put11, double *obj_out, double *put31, double *put32); //my new function void gettumor(int genes, int h); // option h = 1 for one component; h = 2 for two void getnormal1(int genes, unsigned long *seed, int k, int t, double vap1, double del1); // opt 0 for Mu_n, 1 for sigma_n void getnormal2(int genes, unsigned long *seed, int k, int t, double vap1, double del1); void getpi2(int samp, int h,unsigned long *seed); double lf1(int opt, int opt2, double y_sum, double pi_sum, double nval1); double fmin1(double ax, double bx,int iG, int iS, double y_sum, double pi_sum, double (*f)(int, int, double, double, double), double tol); double lf2(int opt, int opt2, double nval2); double fmin2(double ax, double bx,int iG, int iS, double (*f)(int, int, double), double tol); double pf1(double *y_sum, double pi_sum, double *nval1, double pii1, int *Gid1, int size); double pmin1(double ax, double bx, double *y_sum, double pi_sum, double *nval1, int *Gid1, int size, double (*f)(double*, double, double*, double, int*, int), double tol); double pf2(int opt, double *nval1, double *nval2, double pii2, int *Gid2, int size); double pmin2(double ax, double bx,int iS, double *nval1, double *nval2, int *Gid2, int size, double (*f)(int, double*, double*, double, int*, int), double tol); void getmle(int opt, int opt2, int h); ///new function to add double ft_y(double y, double mung, double mutg, double sng, double stg, double pi1, double pi2); double ft_y_SC(double y, double mung, double mutg, double sng, double stg, double pi1, double pi2); double ft_y2(double y, double mung1, double mung2, double mutg, double sng1, double sng2, double stg, double pi1, double pi2); double pf_y(int samp, double pi1); double pf_y2(int samp, double pi1, double pi2); double pmin_y(double ax, double bx, int samp, double (*f)(int, double), double tol); double pmin_y2(double ax, double bx, int samp, double pi2, double (*f)(int, double, double), double tol); double minpi(int samp, double pi2); void getpi(int samp, int h); void getspikeinpi(int samp); void getpiT(int samp); double gammaln(double xx); double gamma(double xx); double tf_y(int genes, double mu, double sigma); double tf_y2(int genes, double mu, double sigma); double mint(int genes, int h, double mu); // opt h =1:one component; h =2:two component double tmin_y(double ax, double bx, int genes, int h, double (*f)(int, int, double), double tol); double tmin_y2(double ax, double bx, int genes, double mu, double (*f)(int, double, double), double tol); double pf_yT(int samp, double pi1, double piT); double NB_nt(double nt, double y, double mung, double mutg, double neta, double teta, double pi1, double pi2); double ft_nb(double y, double mung, double mutg, double neta, double teta, double pi1, double pi2); double ft_nb2(double y, double mung1, double mung2, double mutg, double neta1, double neta2, double teta, double pi1, double pi2); double nitg_ft_y(double t, double y, double mung, double mutg, double sng, double stg, double pi1); double min_nitg_ft_y(double ax, double bx, double y, double mung, double mutg, double sng, double stg, double pi1, double (*f)(double, double, double, double, double, double,double), double tol); ///other functions double pnorm(double tmp1, double *tmp1pos); double cnorm(double val); double dnorm(double val, double mu, double sigma); double sum(double *array, int size); double cinvgamma(double val); // Random Number generation function double kiss(unsigned long *); double snorm(unsigned long *); double rnorm(double,double,unsigned long *); void revsort(double *, int *, int); int ProbSampleReplace(int, double *, unsigned long *); double sd(double *, int); double mean(double *, int); // Variable allocation, arguements for paramters PARAM *p; double **FD, **CD; double **avgparN, **sigparN, **avgparT, **sigparT; double **tmppi1, **tmppi2; int iteration,iteration1; int nP,nG, nS, nSp, nHavepi, Cid, nmle; double vap1, del1, vap1m, del1m; double pvap1, pdel1, pvap1m, pdel1m; int fNorm, fNorm1, fNorm2, intx; // Number of normal samples; int integ; //number of integration bins int opt_seq; // indicator of sequencing technique unsigned long *seed; double M; double cnorm(double val){ double tmpval; tmpval = exp(-pow(val, 2)/20000); return(tmpval); } double cinvgamma(double val){ double tmpval; tmpval = pow(val, -1.001)*exp(-0.001/val); return(tmpval); } double dnorm(double val, double mu, double sigma){ double tmpval; tmpval = exp(-pow((val - mu), 2)/(2*pow(sigma, 2)))/sigma/pow(2*M_PI, 0.5); return(tmpval); } double sum(double *array, int size) { int i; double sum = 0.0; for (i=0;i #include #include #include #include using namespace Rcpp; // [[Rcpp::export]] double ft_y(double y, double mung, double mutg, double sng, double stg, double pi1, double pi2) { int i; double rtval; double tmp; int integ=200; double tmp_pos[integ]; rtval=0; for(i=0;i 1 */ eps = sqrt(eps); a = ax; b = bx; v = a + c * (b - a); w = v; x = v; d = 0.;/* -Wall */ e = 0.; fx = pf_y(y, samp, mung, mutg, sng, stg, x); fv = fx; fw = fx; tol3 = tol / 3.; /* main loop starts here ----------------------------------- */ for(;;) { xm = (a + b) * .5; tol1 = eps * fabs(x) + tol3; t2 = tol1 * 2.; /* check stopping criterion */ if (fabs(x - xm) <= t2 - (b - a) * .5) break; p = 0.; q = 0.; r = 0.; if (fabs(e) > tol1) { /* fit parabola */ r = (x - w) * (fx - fv); q = (x - v) * (fx - fw); p = (x - v) * q - (x - w) * r; q = (q - r) * 2.; if (q > 0.) p = -p; else q = -q; r = e; e = d; } if (fabs(p) >= fabs(q * .5 * r) || p <= q * (a - x) || p >= q * (b - x)) { /* a golden-section step */ if (x < xm) e = b - x; else e = a - x; d = c * e; } else { /* a parabolic-interpolation step */ d = p / q; u = x + d; /* f must not be evaluated too close to ax or bx */ if (u - a < t2 || b - u < t2) { d = tol1; if (x >= xm) d = -d; } } /* f must not be evaluated too close to x */ if (fabs(d) >= tol1) u = x + d; else if (d > 0.) u = x + tol1; else u = x - tol1; fu = pf_y(y, samp, mung, mutg, sng, stg, u); /* update a, b, v, w, and x */ if (fu <= fx) { if (u < x) b = x; else a = x; v = w; w = x; x = u; fv = fw; fw = fx; fx = fu; } else { if (u < x) a = u; else b = u; if (fu <= fw || w == x) { v = w; fv = fw; w = u; fw = fu; } else if (fu <= fv || v == x || v == w) { v = u; fv = fu; } } } /* end of main loop */ return x; } /* Tumor Component Update */ /* void gettumor(int genes, int h) // option h = 1 for one component; h = 2 for two { double mu, sigma,upp; if(opt_seq == 1){ upp = 18; }else{ upp = 1500; } if(h == 1) { mu = tmin_y(1, upp, genes,h, mint, 0.001); sigma = tmin_y2(0.0001, 1, genes, mu, tf_y, 0.001); } p->Tavg[genes] = mu; p->Tsigma[genes] = sigma; } */ // [[Rcpp::export]] double tf_y(int genes, double mu, double sigma, NumericMatrix y, NumericVector pi1, double mung, double sng) { int intx=y.ncol(); double new_like; double tmp; int i; new_like=0.0; for (i=0; i::epsilon(); tol1 = eps + 1.;/* the smallest 1.000... > 1 */ eps = sqrt(eps); a = ax; b = bx; v = a + c * (b - a); w = v; x = v; d = 0.;/* -Wall */ e = 0.; fx = tf_y(genes, mu, x, y, pi1, mung, sng); fv = fx; fw = fx; tol3 = tol / 3.; /* main loop starts here ----------------------------------- */ for(;;) { xm = (a + b) * .5; tol1 = eps * fabs(x) + tol3; t2 = tol1 * 2.; /* check stopping criterion */ if (fabs(x - xm) <= t2 - (b - a) * .5) break; p = 0.; q = 0.; r = 0.; if (fabs(e) > tol1) { /* fit parabola */ r = (x - w) * (fx - fv); q = (x - v) * (fx - fw); p = (x - v) * q - (x - w) * r; q = (q - r) * 2.; if (q > 0.) p = -p; else q = -q; r = e; e = d; } if (fabs(p) >= fabs(q * .5 * r) || p <= q * (a - x) || p >= q * (b - x)) { /* a golden-section step */ if (x < xm) e = b - x; else e = a - x; d = c * e; } else { /* a parabolic-interpolation step */ d = p / q; u = x + d; /* f must not be evaluated too close to ax or bx */ if (u - a < t2 || b - u < t2) { d = tol1; if (x >= xm) d = -d; } } /* f must not be evaluated too close to x */ if (fabs(d) >= tol1) u = x + d; else if (d > 0.) u = x + tol1; else u = x - tol1; fu = tf_y(genes, mu, u, y, pi1, mung, sng); //printf("%f\n",fu); /* update a, b, v, w, and x */ if (fu <= fx) { if (u < x) b = x; else a = x; v = w; w = x; x = u; fv = fw; fw = fx; fx = fu; } else { if (u < x) a = u; else b = u; if (fu <= fw || w == x) { v = w; fv = fw; w = u; fw = fu; } else if (fu <= fv || v == x || v == w) { v = u; fv = fu; } } } /* end of main loop */ return x; } /* Get tumor */ // [[Rcpp::export]] double mint(int genes, double mu, NumericMatrix y, NumericVector pi1, double mung, double sng) // opt h =1:one component; h =2:two component { double sigma; double tmp; sigma = tmin_y2(0.0001, 1, genes, mu, y, pi1, mung, sng, 0.001); tmp = tf_y(genes, mu, sigma, y, pi1, mung, sng); return (tmp); } // [[Rcpp::export]] double tmin_y(double ax, double bx, int genes, NumericMatrix y, NumericVector pi1, double mung, double sng, double tol) { /* c is the squared inverse of the golden ratio */ const double c = (3. - sqrt(5.)) * .5; /* Local variables */ double a, b, d, e, p, q, r, u, v, w, x; double t2, fu, fv, fw, fx, xm, eps, tol1, tol3; /* eps is approximately the square root of the relative machine precision. */ eps = DBL_EPSILON; tol1 = eps + 1.;/* the smallest 1.000... > 1 */ eps = sqrt(eps); a = ax; b = bx; v = a + c * (b - a); w = v; x = v; d = 0.;/* -Wall */ e = 0.; fx = mint(genes, x, y, pi1, mung, sng); fv = fx; fw = fx; tol3 = tol / 3.; /* main loop starts here ----------------------------------- */ for(;;) { xm = (a + b) * .5; tol1 = eps * fabs(x) + tol3; t2 = tol1 * 2.; /* check stopping criterion */ if (fabs(x - xm) <= t2 - (b - a) * .5) break; p = 0.; q = 0.; r = 0.; if (fabs(e) > tol1) { /* fit parabola */ r = (x - w) * (fx - fv); q = (x - v) * (fx - fw); p = (x - v) * q - (x - w) * r; q = (q - r) * 2.; if (q > 0.) p = -p; else q = -q; r = e; e = d; } if (fabs(p) >= fabs(q * .5 * r) || p <= q * (a - x) || p >= q * (b - x)) { /* a golden-section step */ if (x < xm) e = b - x; else e = a - x; d = c * e; } else { /* a parabolic-interpolation step */ d = p / q; u = x + d; /* f must not be evaluated too close to ax or bx */ if (u - a < t2 || b - u < t2) { d = tol1; if (x >= xm) d = -d; } } /* f must not be evaluated too close to x */ if (fabs(d) >= tol1) u = x + d; else if (d > 0.) u = x + tol1; else u = x - tol1; fu = mint(genes, u, y, pi1, mung, sng); /* update a, b, v, w, and x */ if (fu <= fx) { if (u < x) b = x; else a = x; v = w; w = x; x = u; fv = fw; fw = fx; fx = fu; } else { if (u < x) a = u; else b = u; if (fu <= fw || w == x) { v = w; fv = fw; w = u; fw = fu; } else if (fu <= fv || v == x || v == w) { v = u; fv = fu; } } } /* end of main loop */ return x; } // [[Rcpp::export]] double Loglikelihood_ft_y(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { double L=0.0; int S=y.ncol(); int G=y.nrow(); for (int i = 0; i < G; i++){ for (int j=0; j < S; j++){ L=L-ft_y(y(i,j), MuN[i], MuT[i], SigmaN[i], SigmaT[i], Pi[j], 0.0); } } return(L); } // [[Rcpp::export]] double SoftThreshold(double x, double t) { double y=0; if (x>t){ y=x-t; } if (x<(-t)){ y=x+t; } return(y); } // [[Rcpp::export]] NumericVector SoftThreshold_vec(NumericVector x, double t) { int n=x.size(); NumericVector y(n); for(int i=0; it){ y[i]=x[i]-t; } if (x[i]<(-t)){ y[i]=x[i]+t; } } return(y); } // [[Rcpp::export]] NumericVector Gt(NumericVector x, NumericVector Dg, double t) { int n=x.size(); NumericVector y(n); for(int i=0; i < n; i++){ y[i] = ( x[i] - SoftThreshold(x[i]-t*Dg[i], t) )/t; } return(y); } // [[Rcpp::export]] NumericVector Gt_vec(NumericVector x, NumericVector Dg, double t) { int n=x.size(); NumericVector y(n); y=1.0/t*(x - SoftThreshold_vec(x-t*Dg, t) ); return(y); } // [[Rcpp::export]] double Alpha_search_2D(NumericVector x, NumericVector p, int S, int G) { double alpha_max=0.0; double alpha=1.0; for(int j=0; j < S; j++){ if(p[j]<0){ alpha_max=-x[j]/p[j]; }else{ alpha_max=(1.0-x[j])/p[j]; } if(alpha>alpha_max){ alpha=alpha_max; } } for(int i=0; i < 2*G; i++){ if(p[S+i]<0){ alpha_max=-x[S+i]/p[S+i]; if(alpha>alpha_max){ alpha=alpha_max; } } } return(alpha); } // [[Rcpp::export]] double Alpha_search_Pi_2D(NumericVector x, NumericVector p, int S) { double alpha_max=0.0; double alpha=1.0; for(int j=0; j < S; j++){ if(p[j]<0){ alpha_max=-x[j]/p[j]; }else{ alpha_max=(1.0-x[j])/p[j]; } if(alpha>alpha_max){ alpha=alpha_max; } } return(alpha); } // [[Rcpp::export]] double Alpha_search_MuT_2D(NumericVector x, NumericVector p, int G) { double alpha_max=0.0; double alpha=1.0; for(int i=0; i < G; i++){ if(p[i]<0){ alpha_max=-x[i]/p[i]; if(alpha>alpha_max){ alpha=alpha_max; } } } return(alpha); } // [[Rcpp::export]] double Alpha_search_SigmaT_2D(NumericVector x, NumericVector p, int G) { double alpha_max=0.0; double alpha=1.0; for(int i=0; i < G; i++){ if(p[i]<0){ alpha_max=-x[i]/p[i]; }else{ alpha_max=(1.0-x[i])/p[i]; } if(alpha>alpha_max){ alpha=alpha_max; } } return(alpha); } // [[Rcpp::export]] double Alpha_search_2D_sigma(NumericVector x, NumericVector p, int S, int G) { double alpha_max=0.0; double alpha=100.0; for(int j=0; j < S; j++){ if(p[j]<0){ alpha_max=-x[j]/p[j]; }else{ alpha_max=(1.0-x[j])/p[j]; } if(alpha>alpha_max){ alpha=alpha_max; } } for(int i=0; i < G; i++){ if(p[S+2*i]<0){ alpha_max=-x[S+2*i]/p[S+2*i]; if(alpha>alpha_max){ alpha=alpha_max; } } if(p[S+2*i+1]<0){ alpha_max=-x[S+2*i+1]/p[S+2*i+1]; }else{ alpha_max=(1.0-x[S+2*i+1])/p[S+2*i+1]; } if(alpha>alpha_max){ alpha=alpha_max; } } return(alpha); } /* 1st order derivative */ // [[Rcpp::export]] double Unit1(double t, double Mu, double Pit) { return( log2(t)-Mu-log2(Pit) ) ; } // [[Rcpp::export]] NumericVector x_update_2D(NumericVector Pi, NumericVector MuT, NumericVector SigmaT, int S, int G){ NumericVector x(2*G+S); for (int j = 0; j < S; j++){ x[j]=Pi[j]; } for (int i=0; i < G; i++){ x[S+2*i]=MuT[i]; x[S+2*i+1]=SigmaT[i]; } return(x); } // [[Rcpp::export]] List x_update_inv_2D(NumericVector x, int S, int G){ NumericVector Pi(S); NumericVector MuT(G); NumericVector SigmaT(G); for (int j = 0; j < S; j++){ Pi[j]=x[j]; } for (int i=0; i < G; i++){ MuT[i]=x[S+2*i]; SigmaT[i]=x[S+2*i+1]; } return(List::create(Named("Pi")=Pi,Named("MuT")=MuT,Named("SigmaT")=SigmaT)); } /* Likelihood */ // [[Rcpp::export]] double innerCPP_2D(double t, double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT) { return(exp( -log(t) - log(y-t) - 0.5*pow( (log2(t)-MuN-log2(Pi))/SigmaN ,2.0) - 0.5*pow( (log2(y-t)-MuT-log2(1.0-Pi))/SigmaT ,2.0) ) ) ; } // [[Rcpp::export]] double inner_trapez_2D(double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT){ double sum=0.0; int integ=200; double tmp_pos; for(int i=0;i 1e-5){ //printf("%f\n",Gfunc_2D_C(c,x,p,S,G,y,MuN,SigmaN)); if(Loglikelihood_Pi_2D(y,c,MuN,MuT,SigmaN,SigmaT,j) < Loglikelihood_Pi_2D(y,d,MuN,MuT,SigmaN,SigmaT,j)){ b=d; }else{ a=c; } c = b - (b - a) / gr; d = a + (b - a) / gr; i++; } return((c+d)/2); } // [[Rcpp::export]] double Loglikelihood_MuT_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, double MuT, NumericVector SigmaN, NumericVector SigmaT, int i) { double L=0.0; int S=y.ncol(); for (int j=0; j < S; j++){ L=L-log(inner_trapez_2D(y(i,j), Pi[j], MuN[i], MuT, SigmaN[i], SigmaT[i])); } return(L); } // [[Rcpp::export]] double GoldenSection_Loglikelihood_MuT_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector SigmaN, NumericVector SigmaT, int j) { double a=1; double b=18; double gr=(sqrt(5.0) + 1.0) / 2.0; double c = b - (b - a) / gr; double d = a + (b - a) / gr; int i=1; while(fabs(c - d) > 1e-5){ //printf("%f\n",Gfunc_2D_C(c,x,p,S,G,y,MuN,SigmaN)); if(Loglikelihood_MuT_2D(y,Pi,MuN,c,SigmaN,SigmaT,j) < Loglikelihood_MuT_2D(y,Pi,MuN,d,SigmaN,SigmaT,j)){ b=d; }else{ a=c; } c = b - (b - a) / gr; d = a + (b - a) / gr; i++; } return((c+d)/2); } // [[Rcpp::export]] double Loglikelihood_SigmaT_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, double SigmaT, int i) { double L=0.0; int S=y.ncol(); for (int j=0; j < S; j++){ L=L-log(inner_trapez_2D(y(i,j), Pi[j], MuN[i], MuT[i], SigmaN[i], SigmaT)); } return(L); } // [[Rcpp::export]] double GoldenSection_Loglikelihood_SigmaT_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, int j) { double a=0.0001; double b=1.0; double gr=(sqrt(5.0) + 1.0) / 2.0; double c = b - (b - a) / gr; double d = a + (b - a) / gr; int i=1; while(fabs(c - d) > 1e-5){ //printf("%f\n",Gfunc_2D_C(c,x,p,S,G,y,MuN,SigmaN)); if(Loglikelihood_SigmaT_2D(y,Pi,MuN,MuT,SigmaN,c,j) < Loglikelihood_SigmaT_2D(y,Pi,MuN,MuT,SigmaN,d,j)){ b=d; }else{ a=c; } c = b - (b - a) / gr; d = a + (b - a) / gr; i++; } return((c+d)/2); } /* // [[Rcpp::export]] double Opt_Loglikelihood_Pi_2D(Function opt, NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { int S=y.ncol(); List out; NumericVector bound(2); bound[0]=0.01; bound[1]=0.99; double Pi_out; for (int j=0; j < S; j++){ out=opt(Loglikelihood_Pi_2D, bound, 0.001, y, MuN, MuT, SigmaN, SigmaT, j); Pi_out=out[0]; } return(Pi_out); }*/ // [[Rcpp::export]] double Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { double L=0.0; int S=y.ncol(); int G=y.nrow(); for (int i = 0; i < G; i++){ for (int j=0; j < S; j++){ L=L-log(inner_trapez_2D(y(i,j), Pi[j], MuN[i], MuT[i], SigmaN[i], SigmaT[i])); } } return(L); } // [[Rcpp::export]] double Loglikelihood_2D_L1(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector Beta, NumericVector SigmaN, NumericVector SigmaT, double lambda) { double L=0.0; double tmp=0.0; int S=y.ncol(); int G=y.nrow(); for (int i = 0; i < G; i++){ for (int j=0; j < S; j++){ L=L-log(inner_trapez_2D(y(i,j), Pi[j], MuN[i], MuN[i]+Beta[i], SigmaN[i], SigmaT[i])); } tmp=tmp+fabs(Beta[i]); } L=L+lambda*tmp; return(L); } // [[Rcpp::export]] NumericVector D1Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { int S=y.ncol(); // No. of samples int G=y.nrow(); // No. of Genes NumericMatrix f0=f0_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0Pi=D1f0Pi_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0MuT=D1f0MuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0SigmaT=D1f0SigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericVector D1Pi(S); NumericVector D1MuT(G); NumericVector D1SigmaT(G); for (int i = 0; i < G; i++){ D1MuT[i]=0.0; D1SigmaT[i]=0.0; for (int j=0; j < S; j++){ D1MuT[i]=D1MuT[i]-D1f0MuT(i,j)/f0(i,j); D1SigmaT[i]=D1SigmaT[i]-D1f0SigmaT(i,j)/f0(i,j); } } for (int j = 0; j < S; j++){ D1Pi[j]=0.0; for (int i=0; i < G; i++){ D1Pi[j]=D1Pi[j]-D1f0Pi(i,j)/f0(i,j); } } NumericVector D1(2*G+S); for (int j = 0; j < S; j++){ D1[j]=D1Pi[j]; } for (int i=0; i < G; i++){ D1[S+2*i]=D1MuT[i]; D1[S+2*i+1]=D1SigmaT[i]; } return(D1); } // [[Rcpp::export]] NumericVector D1Pi_Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { int S=y.ncol(); // No. of samples int G=y.nrow(); // No. of Genes NumericMatrix f0=f0_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0Pi=D1f0Pi_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericVector D1Pi(S); for (int j = 0; j < S; j++){ D1Pi[j]=0.0; for (int i=0; i < G; i++){ D1Pi[j]=D1Pi[j]-D1f0Pi(i,j)/f0(i,j); } } return(D1Pi); } // [[Rcpp::export]] NumericVector D1MuTSigmaT_Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { int S=y.ncol(); // No. of samples int G=y.nrow(); // No. of Genes NumericMatrix f0=f0_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0MuT=D1f0MuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0SigmaT=D1f0SigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericVector D1MuT(G); NumericVector D1SigmaT(G); for (int i = 0; i < G; i++){ D1MuT[i]=0.0; D1SigmaT[i]=0.0; for (int j=0; j < S; j++){ D1MuT[i]=D1MuT[i]-D1f0MuT(i,j)/f0(i,j); D1SigmaT[i]=D1SigmaT[i]-D1f0SigmaT(i,j)/f0(i,j); } } NumericVector D1(2*G); for (int i=0; i < G; i++){ D1[2*i]=D1MuT[i]; D1[2*i+1]=D1SigmaT[i]; } return(D1); } // [[Rcpp::export]] NumericVector D1MuT_Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { int S=y.ncol(); // No. of samples int G=y.nrow(); // No. of Genes NumericMatrix f0=f0_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0MuT=D1f0MuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericVector D1MuT(G); for (int i = 0; i < G; i++){ for (int j=0; j < S; j++){ D1MuT[i]=D1MuT[i]-D1f0MuT(i,j)/f0(i,j); } } return(D1MuT); } // [[Rcpp::export]] NumericVector D1SigmaT_Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { int S=y.ncol(); // No. of samples int G=y.nrow(); // No. of Genes NumericMatrix f0=f0_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0SigmaT=D1f0SigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericVector D1SigmaT(G); for (int i = 0; i < G; i++){ D1SigmaT[i]=0.0; for (int j=0; j < S; j++){ D1SigmaT[i]=D1SigmaT[i]-D1f0SigmaT(i,j)/f0(i,j); } } return(D1SigmaT); } // [[Rcpp::export]] double log_divide(double x, double f){ double y=0.0; if(x>0.0){ y=exp(log(x)-log(f)); }else{ y=-exp(log(-x)-log(f)); } return(y); } // [[Rcpp::export]] NumericVector D1Loglikelihood_log_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { int S=y.ncol(); // No. of samples int G=y.nrow(); // No. of Genes NumericMatrix f0=f0_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0Pi=D1f0Pi_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0MuT=D1f0MuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0SigmaT=D1f0SigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericVector D1Pi(S); NumericVector D1MuT(G); NumericVector D1SigmaT(G); for (int i = 0; i < G; i++){ D1MuT[i]=0.0; D1SigmaT[i]=0.0; for (int j=0; j < S; j++){ D1MuT[i]=D1MuT[i]-log_divide(D1f0MuT(i,j),f0(i,j)); D1SigmaT[i]=D1SigmaT[i]-log_divide(D1f0SigmaT(i,j),f0(i,j)); } } for (int j = 0; j < S; j++){ D1Pi[j]=0.0; for (int i=0; i < G; i++){ D1Pi[j]=D1Pi[j]-log_divide(D1f0Pi(i,j),f0(i,j)); } } NumericVector D1(2*G+S); for (int j = 0; j < S; j++){ D1[j]=D1Pi[j]; } for (int i=0; i < G; i++){ D1[S+2*i]=D1MuT[i]; D1[S+2*i+1]=D1SigmaT[i]; } return(D1); } // [[Rcpp::export]] NumericMatrix D2Loglikelihood_log_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { int S=y.ncol(); // No. of samples int G=y.nrow(); // No. of Genes NumericMatrix Hessian(2*G+S,2*G+S); NumericMatrix f0=f0_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0Pi=D1f0Pi_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0MuT=D1f0MuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0SigmaT=D1f0SigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0Pi=D2f0Pi_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0PiMuT=D2f0PiMuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0PiSigmaT=D2f0PiSigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0MuT=D2f0MuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0MuTSigmaT=D2f0MuTSigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0SigmaT=D2f0SigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericVector D2Pi(S); NumericVector D2MuT(G); NumericVector D2SigmaT(G); for (int j = 0; j < S; j++){ D2Pi[j]=0.0; for (int i=0; i < G; i++){ D2Pi[j]=D2Pi[j]+log_divide((log_divide(pow(D1f0Pi(i,j),2.0),f0(i,j))-D2f0Pi(i,j)),f0(i,j)); Hessian(j,S+2*i)=log_divide((log_divide(D1f0Pi(i,j)*D1f0MuT(i,j),f0(i,j))-D2f0PiMuT(i,j)),f0(i,j)); //D2PiMuT Hessian(S+2*i,j)=Hessian(j,S+2*i); Hessian(j,S+2*i+1)=log_divide((log_divide(D1f0Pi(i,j)*D1f0SigmaT(i,j),f0(i,j))-D2f0PiSigmaT(i,j)),f0(i,j)); //D2PiSigmaT Hessian(S+2*i+1,j)=Hessian(j,S+2*i+1); } } for (int i = 0; i < G; i++){ D2MuT[i]=0.0; D2SigmaT[i]=0.0; Hessian(S+2*i,S+2*i+1)=0.0; for (int j=0; j < S; j++){ D2MuT[i]=D2MuT[i]+log_divide((log_divide(pow(D1f0MuT(i,j),2.0),f0(i,j))-D2f0MuT(i,j)),f0(i,j)); D2SigmaT[i]=D2SigmaT[i]+log_divide((log_divide(pow(D1f0SigmaT(i,j),2.0),f0(i,j))-D2f0SigmaT(i,j)),f0(i,j)); Hessian(S+2*i,S+2*i+1)=Hessian(S+2*i,S+2*i+1)+log_divide((log_divide(D1f0MuT(i,j)*D1f0SigmaT(i,j),f0(i,j))-D2f0MuTSigmaT(i,j)),f0(i,j)); } Hessian(S+2*i+1,S+2*i)=Hessian(S+2*i,S+2*i+1); } for (int j = 0; j < S; j++){ Hessian(j,j)=D2Pi[j]; } for (int i=0; i < G; i++){ Hessian(S+2*i,S+2*i)=D2MuT[i]; Hessian(S+2*i+1,S+2*i+1)=D2SigmaT[i]; } return(Hessian); } // [[Rcpp::export]] NumericMatrix D2Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { int S=y.ncol(); // No. of samples int G=y.nrow(); // No. of Genes NumericMatrix Hessian(2*G+S,2*G+S); NumericMatrix f0=f0_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0Pi=D1f0Pi_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0MuT=D1f0MuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0SigmaT=D1f0SigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0Pi=D2f0Pi_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0PiMuT=D2f0PiMuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0PiSigmaT=D2f0PiSigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0MuT=D2f0MuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0MuTSigmaT=D2f0MuTSigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0SigmaT=D2f0SigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericVector D2Pi(S); NumericVector D2MuT(G); NumericVector D2SigmaT(G); for (int j = 0; j < S; j++){ D2Pi[j]=0.0; for (int i=0; i < G; i++){ D2Pi[j]=D2Pi[j]+(pow(D1f0Pi(i,j),2.0)/f0(i,j)-D2f0Pi(i,j))/f0(i,j); Hessian(j,S+2*i)=(D1f0Pi(i,j)*D1f0MuT(i,j)/f0(i,j)-D2f0PiMuT(i,j))/f0(i,j); //D2PiMuT Hessian(S+2*i,j)=Hessian(j,S+2*i); Hessian(j,S+2*i+1)=(D1f0Pi(i,j)*D1f0SigmaT(i,j)/f0(i,j)-D2f0PiSigmaT(i,j))/f0(i,j); //D2PiSigmaT Hessian(S+2*i+1,j)=Hessian(j,S+2*i+1); } } for (int i = 0; i < G; i++){ D2MuT[i]=0.0; D2SigmaT[i]=0.0; Hessian(S+2*i,S+2*i+1)=0.0; for (int j=0; j < S; j++){ D2MuT[i]=D2MuT[i]+(pow(D1f0MuT(i,j),2.0)/f0(i,j)-D2f0MuT(i,j))/f0(i,j); D2SigmaT[i]=D2SigmaT[i]+(pow(D1f0SigmaT(i,j),2.0)/f0(i,j)-D2f0SigmaT(i,j))/f0(i,j); Hessian(S+2*i,S+2*i+1)=Hessian(S+2*i,S+2*i+1)+(D1f0MuT(i,j)*D1f0SigmaT(i,j)/f0(i,j)-D2f0MuTSigmaT(i,j))/f0(i,j); } Hessian(S+2*i+1,S+2*i)=Hessian(S+2*i,S+2*i+1); } for (int j = 0; j < S; j++){ Hessian(j,j)=D2Pi[j]; } for (int i=0; i < G; i++){ Hessian(S+2*i,S+2*i)=D2MuT[i]; Hessian(S+2*i+1,S+2*i+1)=D2SigmaT[i]; } return(Hessian); } // [[Rcpp::export]] List D2Loglikelihood_unit_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT) { int S=y.ncol(); // No. of samples int G=y.nrow(); // No. of Genes NumericMatrix f0=f0_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0Pi=D1f0Pi_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0MuT=D1f0MuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D1f0SigmaT=D1f0SigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0Pi=D2f0Pi_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0PiMuT=D2f0PiMuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0PiSigmaT=D2f0PiSigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0MuT=D2f0MuT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0MuTSigmaT=D2f0MuTSigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2f0SigmaT=D2f0SigmaT_func_2D(y,Pi,MuN,MuT,SigmaN,SigmaT); NumericMatrix D2Pi(G,S); NumericMatrix D2PiMuT(G,S); NumericMatrix D2PiSigmaT(G,S); NumericMatrix D2MuT(G,S); NumericMatrix D2MuTSigmaT(G,S); NumericMatrix D2SigmaT(G,S); for (int j = 0; j < S; j++){ for (int i=0; i < G; i++){ D2Pi(i,j)=(pow(D1f0Pi(i,j),2.0)/f0(i,j)-D2f0Pi(i,j))/f0(i,j); D2PiMuT(i,j)=(D1f0Pi(i,j)*D1f0MuT(i,j)/f0(i,j)-D2f0PiMuT(i,j))/f0(i,j); D2PiSigmaT(i,j)=(D1f0Pi(i,j)*D1f0SigmaT(i,j)/f0(i,j)-D2f0PiSigmaT(i,j))/f0(i,j); D2MuT(i,j)=(pow(D1f0MuT(i,j),2.0)/f0(i,j)-D2f0MuT(i,j))/f0(i,j); D2MuTSigmaT(i,j)=(D1f0MuT(i,j)*D1f0SigmaT(i,j)/f0(i,j)-D2f0MuTSigmaT(i,j))/f0(i,j); D2SigmaT(i,j)=(pow(D1f0SigmaT(i,j),2.0)/f0(i,j)-D2f0SigmaT(i,j))/f0(i,j); } } return(List::create(Named("D2Pi")=D2Pi,Named("D2PiMuT")=D2PiMuT,Named("D2PiSigmaT")=D2PiSigmaT,Named("D2MuT")=D2MuT,Named("D2MuTSigmaT")=D2MuTSigmaT,Named("D2SigmaT")=D2SigmaT)); } // [[Rcpp::export]] double Gfunc_2D_C(double alpha, NumericVector x, NumericVector p, int S, int G, NumericMatrix y,NumericVector MuN, NumericVector SigmaN){ NumericVector x_mid=x+alpha*p; List L=x_update_inv_2D(x_mid,S,G); NumericVector Pi_mid=L[0]; NumericVector MuT_mid=L[1]; NumericVector SigmaT_mid=L[2]; double obj_mid=Loglikelihood_2D(y, Pi_mid, MuN, MuT_mid, SigmaN, SigmaT_mid); return(obj_mid); } // [[Rcpp::export]] double GoldenLine_search_2D(double alpha, NumericVector x, NumericVector p, NumericMatrix y, NumericVector MuN, NumericVector SigmaN, int S, int G, double steps) { double a=0.0; double b=alpha; double gr=(sqrt(5.0) + 1.0) / 2.0; double c = b - (b - a) / gr; double d = a + (b - a) / gr; int i=1; while(fabs(c - d) > (1e-5)/steps){ //printf("%f\n",Gfunc_2D_C(c,x,p,S,G,y,MuN,SigmaN)); if(Gfunc_2D_C(c,x,p,S,G,y,MuN,SigmaN) < Gfunc_2D_C(d,x,p,S,G,y,MuN,SigmaN)){ b=d; }else{ a=c; } c = b - (b - a) / gr; d = a + (b - a) / gr; i++; } return((c+d)/2); } // [[Rcpp::export]] double GoldenLine_search_Pi_2D(double alpha, NumericVector x, NumericVector p, NumericMatrix y, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT, double steps) { double a=0.0; double b=alpha; double gr=(sqrt(5.0) + 1.0) / 2.0; double c = b - (b - a) / gr; double d = a + (b - a) / gr; int i=1; while(fabs(c - d) > (1e-5)/steps){ NumericVector Pi_c=x+c*p; NumericVector Pi_d=x+d*p; if(Loglikelihood_2D(y, Pi_c, MuN, MuT, SigmaN, SigmaT) < Loglikelihood_2D(y, Pi_d, MuN, MuT, SigmaN, SigmaT)){ b=d; }else{ a=c; } c = b - (b - a) / gr; d = a + (b - a) / gr; i++; } return((c+d)/2); } // [[Rcpp::export]] double GoldenLine_search_MuT_2D(double alpha, NumericVector x, NumericVector p, NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector SigmaN, NumericVector SigmaT, double steps) { double a=0.0; double b=alpha; double gr=(sqrt(5.0) + 1.0) / 2.0; double c = b - (b - a) / gr; double d = a + (b - a) / gr; int i=1; while(fabs(c - d) > (1e-5)/steps){ NumericVector MuT_c=x+c*p; NumericVector MuT_d=x+d*p; if(Loglikelihood_2D(y, Pi, MuN, MuT_c, SigmaN, SigmaT) < Loglikelihood_2D(y, Pi, MuN, MuT_d, SigmaN, SigmaT)){ b=d; }else{ a=c; } c = b - (b - a) / gr; d = a + (b - a) / gr; i++; } return((c+d)/2); } // [[Rcpp::export]] double GoldenLine_search_SigmaT_2D(double alpha, NumericVector x, NumericVector p, NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, double steps) { double a=0.0; double b=alpha; double gr=(sqrt(5.0) + 1.0) / 2.0; double c = b - (b - a) / gr; double d = a + (b - a) / gr; int i=1; while(fabs(c - d) > (1e-5)/steps){ NumericVector SigmaT_c=x+c*p; NumericVector SigmaT_d=x+d*p; if(Loglikelihood_2D(y, Pi, MuN, MuT, SigmaN, SigmaT_c) < Loglikelihood_2D(y, Pi, MuN, MuT, SigmaN, SigmaT_d)){ b=d; }else{ a=c; } c = b - (b - a) / gr; d = a + (b - a) / gr; i++; } return((c+d)/2); } DeMixT/src/RcppExports.cpp0000644000175200017520000017656714710233064016540 0ustar00biocbuildbiocbuild// Generated by using Rcpp::compileAttributes() -> do not edit by hand // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include using namespace Rcpp; #ifdef RCPP_USE_GLOBAL_ROSTREAM Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); #endif // ft_y double ft_y(double y, double mung, double mutg, double sng, double stg, double pi1, double pi2); RcppExport SEXP _DeMixT_ft_y(SEXP ySEXP, SEXP mungSEXP, SEXP mutgSEXP, SEXP sngSEXP, SEXP stgSEXP, SEXP pi1SEXP, SEXP pi2SEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type mung(mungSEXP); Rcpp::traits::input_parameter< double >::type mutg(mutgSEXP); Rcpp::traits::input_parameter< double >::type sng(sngSEXP); Rcpp::traits::input_parameter< double >::type stg(stgSEXP); Rcpp::traits::input_parameter< double >::type pi1(pi1SEXP); Rcpp::traits::input_parameter< double >::type pi2(pi2SEXP); rcpp_result_gen = Rcpp::wrap(ft_y(y, mung, mutg, sng, stg, pi1, pi2)); return rcpp_result_gen; END_RCPP } // pf_y double pf_y(NumericMatrix y, int samp, NumericVector mung, NumericVector mutg, NumericVector sng, NumericVector stg, double pi1); RcppExport SEXP _DeMixT_pf_y(SEXP ySEXP, SEXP sampSEXP, SEXP mungSEXP, SEXP mutgSEXP, SEXP sngSEXP, SEXP stgSEXP, SEXP pi1SEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< int >::type samp(sampSEXP); Rcpp::traits::input_parameter< NumericVector >::type mung(mungSEXP); Rcpp::traits::input_parameter< NumericVector >::type mutg(mutgSEXP); Rcpp::traits::input_parameter< NumericVector >::type sng(sngSEXP); Rcpp::traits::input_parameter< NumericVector >::type stg(stgSEXP); Rcpp::traits::input_parameter< double >::type pi1(pi1SEXP); rcpp_result_gen = Rcpp::wrap(pf_y(y, samp, mung, mutg, sng, stg, pi1)); return rcpp_result_gen; END_RCPP } // pmin_y double pmin_y(double ax, double bx, int samp, NumericMatrix y, NumericVector mung, NumericVector mutg, NumericVector sng, NumericVector stg, double tol); RcppExport SEXP _DeMixT_pmin_y(SEXP axSEXP, SEXP bxSEXP, SEXP sampSEXP, SEXP ySEXP, SEXP mungSEXP, SEXP mutgSEXP, SEXP sngSEXP, SEXP stgSEXP, SEXP tolSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type ax(axSEXP); Rcpp::traits::input_parameter< double >::type bx(bxSEXP); Rcpp::traits::input_parameter< int >::type samp(sampSEXP); Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type mung(mungSEXP); Rcpp::traits::input_parameter< NumericVector >::type mutg(mutgSEXP); Rcpp::traits::input_parameter< NumericVector >::type sng(sngSEXP); Rcpp::traits::input_parameter< NumericVector >::type stg(stgSEXP); Rcpp::traits::input_parameter< double >::type tol(tolSEXP); rcpp_result_gen = Rcpp::wrap(pmin_y(ax, bx, samp, y, mung, mutg, sng, stg, tol)); return rcpp_result_gen; END_RCPP } // tf_y double tf_y(int genes, double mu, double sigma, NumericMatrix y, NumericVector pi1, double mung, double sng); RcppExport SEXP _DeMixT_tf_y(SEXP genesSEXP, SEXP muSEXP, SEXP sigmaSEXP, SEXP ySEXP, SEXP pi1SEXP, SEXP mungSEXP, SEXP sngSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< int >::type genes(genesSEXP); Rcpp::traits::input_parameter< double >::type mu(muSEXP); Rcpp::traits::input_parameter< double >::type sigma(sigmaSEXP); Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type pi1(pi1SEXP); Rcpp::traits::input_parameter< double >::type mung(mungSEXP); Rcpp::traits::input_parameter< double >::type sng(sngSEXP); rcpp_result_gen = Rcpp::wrap(tf_y(genes, mu, sigma, y, pi1, mung, sng)); return rcpp_result_gen; END_RCPP } // tmin_y2 double tmin_y2(double ax, double bx, int genes, double mu, NumericMatrix y, NumericVector pi1, double mung, double sng, double tol); RcppExport SEXP _DeMixT_tmin_y2(SEXP axSEXP, SEXP bxSEXP, SEXP genesSEXP, SEXP muSEXP, SEXP ySEXP, SEXP pi1SEXP, SEXP mungSEXP, SEXP sngSEXP, SEXP tolSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type ax(axSEXP); Rcpp::traits::input_parameter< double >::type bx(bxSEXP); Rcpp::traits::input_parameter< int >::type genes(genesSEXP); Rcpp::traits::input_parameter< double >::type mu(muSEXP); Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type pi1(pi1SEXP); Rcpp::traits::input_parameter< double >::type mung(mungSEXP); Rcpp::traits::input_parameter< double >::type sng(sngSEXP); Rcpp::traits::input_parameter< double >::type tol(tolSEXP); rcpp_result_gen = Rcpp::wrap(tmin_y2(ax, bx, genes, mu, y, pi1, mung, sng, tol)); return rcpp_result_gen; END_RCPP } // mint double mint(int genes, double mu, NumericMatrix y, NumericVector pi1, double mung, double sng); RcppExport SEXP _DeMixT_mint(SEXP genesSEXP, SEXP muSEXP, SEXP ySEXP, SEXP pi1SEXP, SEXP mungSEXP, SEXP sngSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< int >::type genes(genesSEXP); Rcpp::traits::input_parameter< double >::type mu(muSEXP); Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type pi1(pi1SEXP); Rcpp::traits::input_parameter< double >::type mung(mungSEXP); Rcpp::traits::input_parameter< double >::type sng(sngSEXP); rcpp_result_gen = Rcpp::wrap(mint(genes, mu, y, pi1, mung, sng)); return rcpp_result_gen; END_RCPP } // tmin_y double tmin_y(double ax, double bx, int genes, NumericMatrix y, NumericVector pi1, double mung, double sng, double tol); RcppExport SEXP _DeMixT_tmin_y(SEXP axSEXP, SEXP bxSEXP, SEXP genesSEXP, SEXP ySEXP, SEXP pi1SEXP, SEXP mungSEXP, SEXP sngSEXP, SEXP tolSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type ax(axSEXP); Rcpp::traits::input_parameter< double >::type bx(bxSEXP); Rcpp::traits::input_parameter< int >::type genes(genesSEXP); Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type pi1(pi1SEXP); Rcpp::traits::input_parameter< double >::type mung(mungSEXP); Rcpp::traits::input_parameter< double >::type sng(sngSEXP); Rcpp::traits::input_parameter< double >::type tol(tolSEXP); rcpp_result_gen = Rcpp::wrap(tmin_y(ax, bx, genes, y, pi1, mung, sng, tol)); return rcpp_result_gen; END_RCPP } // Loglikelihood_ft_y double Loglikelihood_ft_y(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_Loglikelihood_ft_y(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(Loglikelihood_ft_y(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // SoftThreshold double SoftThreshold(double x, double t); RcppExport SEXP _DeMixT_SoftThreshold(SEXP xSEXP, SEXP tSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type x(xSEXP); Rcpp::traits::input_parameter< double >::type t(tSEXP); rcpp_result_gen = Rcpp::wrap(SoftThreshold(x, t)); return rcpp_result_gen; END_RCPP } // SoftThreshold_vec NumericVector SoftThreshold_vec(NumericVector x, double t); RcppExport SEXP _DeMixT_SoftThreshold_vec(SEXP xSEXP, SEXP tSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< double >::type t(tSEXP); rcpp_result_gen = Rcpp::wrap(SoftThreshold_vec(x, t)); return rcpp_result_gen; END_RCPP } // Gt NumericVector Gt(NumericVector x, NumericVector Dg, double t); RcppExport SEXP _DeMixT_Gt(SEXP xSEXP, SEXP DgSEXP, SEXP tSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type Dg(DgSEXP); Rcpp::traits::input_parameter< double >::type t(tSEXP); rcpp_result_gen = Rcpp::wrap(Gt(x, Dg, t)); return rcpp_result_gen; END_RCPP } // Gt_vec NumericVector Gt_vec(NumericVector x, NumericVector Dg, double t); RcppExport SEXP _DeMixT_Gt_vec(SEXP xSEXP, SEXP DgSEXP, SEXP tSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type Dg(DgSEXP); Rcpp::traits::input_parameter< double >::type t(tSEXP); rcpp_result_gen = Rcpp::wrap(Gt_vec(x, Dg, t)); return rcpp_result_gen; END_RCPP } // Alpha_search_2D double Alpha_search_2D(NumericVector x, NumericVector p, int S, int G); RcppExport SEXP _DeMixT_Alpha_search_2D(SEXP xSEXP, SEXP pSEXP, SEXP SSEXP, SEXP GSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type p(pSEXP); Rcpp::traits::input_parameter< int >::type S(SSEXP); Rcpp::traits::input_parameter< int >::type G(GSEXP); rcpp_result_gen = Rcpp::wrap(Alpha_search_2D(x, p, S, G)); return rcpp_result_gen; END_RCPP } // Alpha_search_Pi_2D double Alpha_search_Pi_2D(NumericVector x, NumericVector p, int S); RcppExport SEXP _DeMixT_Alpha_search_Pi_2D(SEXP xSEXP, SEXP pSEXP, SEXP SSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type p(pSEXP); Rcpp::traits::input_parameter< int >::type S(SSEXP); rcpp_result_gen = Rcpp::wrap(Alpha_search_Pi_2D(x, p, S)); return rcpp_result_gen; END_RCPP } // Alpha_search_MuT_2D double Alpha_search_MuT_2D(NumericVector x, NumericVector p, int G); RcppExport SEXP _DeMixT_Alpha_search_MuT_2D(SEXP xSEXP, SEXP pSEXP, SEXP GSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type p(pSEXP); Rcpp::traits::input_parameter< int >::type G(GSEXP); rcpp_result_gen = Rcpp::wrap(Alpha_search_MuT_2D(x, p, G)); return rcpp_result_gen; END_RCPP } // Alpha_search_SigmaT_2D double Alpha_search_SigmaT_2D(NumericVector x, NumericVector p, int G); RcppExport SEXP _DeMixT_Alpha_search_SigmaT_2D(SEXP xSEXP, SEXP pSEXP, SEXP GSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type p(pSEXP); Rcpp::traits::input_parameter< int >::type G(GSEXP); rcpp_result_gen = Rcpp::wrap(Alpha_search_SigmaT_2D(x, p, G)); return rcpp_result_gen; END_RCPP } // Alpha_search_2D_sigma double Alpha_search_2D_sigma(NumericVector x, NumericVector p, int S, int G); RcppExport SEXP _DeMixT_Alpha_search_2D_sigma(SEXP xSEXP, SEXP pSEXP, SEXP SSEXP, SEXP GSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type p(pSEXP); Rcpp::traits::input_parameter< int >::type S(SSEXP); Rcpp::traits::input_parameter< int >::type G(GSEXP); rcpp_result_gen = Rcpp::wrap(Alpha_search_2D_sigma(x, p, S, G)); return rcpp_result_gen; END_RCPP } // Unit1 double Unit1(double t, double Mu, double Pit); RcppExport SEXP _DeMixT_Unit1(SEXP tSEXP, SEXP MuSEXP, SEXP PitSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type t(tSEXP); Rcpp::traits::input_parameter< double >::type Mu(MuSEXP); Rcpp::traits::input_parameter< double >::type Pit(PitSEXP); rcpp_result_gen = Rcpp::wrap(Unit1(t, Mu, Pit)); return rcpp_result_gen; END_RCPP } // x_update_2D NumericVector x_update_2D(NumericVector Pi, NumericVector MuT, NumericVector SigmaT, int S, int G); RcppExport SEXP _DeMixT_x_update_2D(SEXP PiSEXP, SEXP MuTSEXP, SEXP SigmaTSEXP, SEXP SSEXP, SEXP GSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); Rcpp::traits::input_parameter< int >::type S(SSEXP); Rcpp::traits::input_parameter< int >::type G(GSEXP); rcpp_result_gen = Rcpp::wrap(x_update_2D(Pi, MuT, SigmaT, S, G)); return rcpp_result_gen; END_RCPP } // x_update_inv_2D List x_update_inv_2D(NumericVector x, int S, int G); RcppExport SEXP _DeMixT_x_update_inv_2D(SEXP xSEXP, SEXP SSEXP, SEXP GSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< int >::type S(SSEXP); Rcpp::traits::input_parameter< int >::type G(GSEXP); rcpp_result_gen = Rcpp::wrap(x_update_inv_2D(x, S, G)); return rcpp_result_gen; END_RCPP } // innerCPP_2D double innerCPP_2D(double t, double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_innerCPP_2D(SEXP tSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type t(tSEXP); Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(innerCPP_2D(t, y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // inner_trapez_2D double inner_trapez_2D(double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_inner_trapez_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(inner_trapez_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // DPi_inner_2D double DPi_inner_2D(double t, double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_DPi_inner_2D(SEXP tSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type t(tSEXP); Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(DPi_inner_2D(t, y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // DPi_outer_2D double DPi_outer_2D(double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_DPi_outer_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(DPi_outer_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D1f0Pi_func_2D NumericMatrix D1f0Pi_func_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D1f0Pi_func_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D1f0Pi_func_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // DMuT_inner_2D double DMuT_inner_2D(double t, double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_DMuT_inner_2D(SEXP tSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type t(tSEXP); Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(DMuT_inner_2D(t, y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // DMuT_outer_2D double DMuT_outer_2D(double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_DMuT_outer_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(DMuT_outer_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D1f0MuT_func_2D NumericMatrix D1f0MuT_func_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D1f0MuT_func_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D1f0MuT_func_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // DSigmaT_inner_2D double DSigmaT_inner_2D(double t, double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_DSigmaT_inner_2D(SEXP tSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type t(tSEXP); Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(DSigmaT_inner_2D(t, y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // DSigmaT_outer_2D double DSigmaT_outer_2D(double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_DSigmaT_outer_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(DSigmaT_outer_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D1f0SigmaT_func_2D NumericMatrix D1f0SigmaT_func_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D1f0SigmaT_func_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D1f0SigmaT_func_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2Pi_inner_2D double D2Pi_inner_2D(double t, double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT, double ln2); RcppExport SEXP _DeMixT_D2Pi_inner_2D(SEXP tSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP, SEXP ln2SEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type t(tSEXP); Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); Rcpp::traits::input_parameter< double >::type ln2(ln2SEXP); rcpp_result_gen = Rcpp::wrap(D2Pi_inner_2D(t, y, Pi, MuN, MuT, SigmaN, SigmaT, ln2)); return rcpp_result_gen; END_RCPP } // D2Pi_outer_2D double D2Pi_outer_2D(double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_D2Pi_outer_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2Pi_outer_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2f0Pi_func_2D NumericMatrix D2f0Pi_func_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D2f0Pi_func_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2f0Pi_func_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2PiMuT_inner_2D double D2PiMuT_inner_2D(double t, double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_D2PiMuT_inner_2D(SEXP tSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type t(tSEXP); Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2PiMuT_inner_2D(t, y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2PiMuT_outer_2D double D2PiMuT_outer_2D(double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_D2PiMuT_outer_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2PiMuT_outer_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2f0PiMuT_func_2D NumericMatrix D2f0PiMuT_func_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D2f0PiMuT_func_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2f0PiMuT_func_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2PiSigmaT_inner_2D double D2PiSigmaT_inner_2D(double t, double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_D2PiSigmaT_inner_2D(SEXP tSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type t(tSEXP); Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2PiSigmaT_inner_2D(t, y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2PiSigmaT_outer_2D double D2PiSigmaT_outer_2D(double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_D2PiSigmaT_outer_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2PiSigmaT_outer_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2f0PiSigmaT_func_2D NumericMatrix D2f0PiSigmaT_func_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D2f0PiSigmaT_func_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2f0PiSigmaT_func_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2MuT_inner_2D double D2MuT_inner_2D(double t, double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_D2MuT_inner_2D(SEXP tSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type t(tSEXP); Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2MuT_inner_2D(t, y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2MuT_outer_2D double D2MuT_outer_2D(double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_D2MuT_outer_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2MuT_outer_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2f0MuT_func_2D NumericMatrix D2f0MuT_func_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D2f0MuT_func_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2f0MuT_func_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2MuTSigmaT_inner_2D double D2MuTSigmaT_inner_2D(double t, double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_D2MuTSigmaT_inner_2D(SEXP tSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type t(tSEXP); Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2MuTSigmaT_inner_2D(t, y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2MuTSigmaT_outer_2D double D2MuTSigmaT_outer_2D(double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_D2MuTSigmaT_outer_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2MuTSigmaT_outer_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2f0MuTSigmaT_func_2D NumericMatrix D2f0MuTSigmaT_func_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D2f0MuTSigmaT_func_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2f0MuTSigmaT_func_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2SigmaT_inner_2D double D2SigmaT_inner_2D(double t, double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_D2SigmaT_inner_2D(SEXP tSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type t(tSEXP); Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2SigmaT_inner_2D(t, y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2SigmaT_outer_2D double D2SigmaT_outer_2D(double y, double Pi, double MuN, double MuT, double SigmaN, double SigmaT); RcppExport SEXP _DeMixT_D2SigmaT_outer_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< double >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< double >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2SigmaT_outer_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2f0SigmaT_func_2D NumericMatrix D2f0SigmaT_func_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D2f0SigmaT_func_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2f0SigmaT_func_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // f0_func_2D NumericMatrix f0_func_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_f0_func_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(f0_func_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // Loglikelihood_Pi_2D double Loglikelihood_Pi_2D(NumericMatrix y, double Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT, int j); RcppExport SEXP _DeMixT_Loglikelihood_Pi_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP, SEXP jSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< double >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); Rcpp::traits::input_parameter< int >::type j(jSEXP); rcpp_result_gen = Rcpp::wrap(Loglikelihood_Pi_2D(y, Pi, MuN, MuT, SigmaN, SigmaT, j)); return rcpp_result_gen; END_RCPP } // GoldenSection_Loglikelihood_Pi_2D double GoldenSection_Loglikelihood_Pi_2D(NumericMatrix y, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT, int j); RcppExport SEXP _DeMixT_GoldenSection_Loglikelihood_Pi_2D(SEXP ySEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP, SEXP jSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); Rcpp::traits::input_parameter< int >::type j(jSEXP); rcpp_result_gen = Rcpp::wrap(GoldenSection_Loglikelihood_Pi_2D(y, MuN, MuT, SigmaN, SigmaT, j)); return rcpp_result_gen; END_RCPP } // Loglikelihood_MuT_2D double Loglikelihood_MuT_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, double MuT, NumericVector SigmaN, NumericVector SigmaT, int i); RcppExport SEXP _DeMixT_Loglikelihood_MuT_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP, SEXP iSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< double >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); Rcpp::traits::input_parameter< int >::type i(iSEXP); rcpp_result_gen = Rcpp::wrap(Loglikelihood_MuT_2D(y, Pi, MuN, MuT, SigmaN, SigmaT, i)); return rcpp_result_gen; END_RCPP } // GoldenSection_Loglikelihood_MuT_2D double GoldenSection_Loglikelihood_MuT_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector SigmaN, NumericVector SigmaT, int j); RcppExport SEXP _DeMixT_GoldenSection_Loglikelihood_MuT_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP, SEXP jSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); Rcpp::traits::input_parameter< int >::type j(jSEXP); rcpp_result_gen = Rcpp::wrap(GoldenSection_Loglikelihood_MuT_2D(y, Pi, MuN, SigmaN, SigmaT, j)); return rcpp_result_gen; END_RCPP } // Loglikelihood_SigmaT_2D double Loglikelihood_SigmaT_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, double SigmaT, int i); RcppExport SEXP _DeMixT_Loglikelihood_SigmaT_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP, SEXP iSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type SigmaT(SigmaTSEXP); Rcpp::traits::input_parameter< int >::type i(iSEXP); rcpp_result_gen = Rcpp::wrap(Loglikelihood_SigmaT_2D(y, Pi, MuN, MuT, SigmaN, SigmaT, i)); return rcpp_result_gen; END_RCPP } // GoldenSection_Loglikelihood_SigmaT_2D double GoldenSection_Loglikelihood_SigmaT_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, int j); RcppExport SEXP _DeMixT_GoldenSection_Loglikelihood_SigmaT_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP jSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< int >::type j(jSEXP); rcpp_result_gen = Rcpp::wrap(GoldenSection_Loglikelihood_SigmaT_2D(y, Pi, MuN, MuT, SigmaN, j)); return rcpp_result_gen; END_RCPP } // Loglikelihood_2D double Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_Loglikelihood_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(Loglikelihood_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // Loglikelihood_2D_L1 double Loglikelihood_2D_L1(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector Beta, NumericVector SigmaN, NumericVector SigmaT, double lambda); RcppExport SEXP _DeMixT_Loglikelihood_2D_L1(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP BetaSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP, SEXP lambdaSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type Beta(BetaSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP); rcpp_result_gen = Rcpp::wrap(Loglikelihood_2D_L1(y, Pi, MuN, Beta, SigmaN, SigmaT, lambda)); return rcpp_result_gen; END_RCPP } // D1Loglikelihood_2D NumericVector D1Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D1Loglikelihood_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D1Loglikelihood_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D1Pi_Loglikelihood_2D NumericVector D1Pi_Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D1Pi_Loglikelihood_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D1Pi_Loglikelihood_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D1MuTSigmaT_Loglikelihood_2D NumericVector D1MuTSigmaT_Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D1MuTSigmaT_Loglikelihood_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D1MuTSigmaT_Loglikelihood_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D1MuT_Loglikelihood_2D NumericVector D1MuT_Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D1MuT_Loglikelihood_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D1MuT_Loglikelihood_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D1SigmaT_Loglikelihood_2D NumericVector D1SigmaT_Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D1SigmaT_Loglikelihood_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D1SigmaT_Loglikelihood_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // log_divide double log_divide(double x, double f); RcppExport SEXP _DeMixT_log_divide(SEXP xSEXP, SEXP fSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type x(xSEXP); Rcpp::traits::input_parameter< double >::type f(fSEXP); rcpp_result_gen = Rcpp::wrap(log_divide(x, f)); return rcpp_result_gen; END_RCPP } // D1Loglikelihood_log_2D NumericVector D1Loglikelihood_log_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D1Loglikelihood_log_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D1Loglikelihood_log_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2Loglikelihood_log_2D NumericMatrix D2Loglikelihood_log_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D2Loglikelihood_log_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2Loglikelihood_log_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2Loglikelihood_2D NumericMatrix D2Loglikelihood_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D2Loglikelihood_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2Loglikelihood_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // D2Loglikelihood_unit_2D List D2Loglikelihood_unit_2D(NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT); RcppExport SEXP _DeMixT_D2Loglikelihood_unit_2D(SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); rcpp_result_gen = Rcpp::wrap(D2Loglikelihood_unit_2D(y, Pi, MuN, MuT, SigmaN, SigmaT)); return rcpp_result_gen; END_RCPP } // Gfunc_2D_C double Gfunc_2D_C(double alpha, NumericVector x, NumericVector p, int S, int G, NumericMatrix y, NumericVector MuN, NumericVector SigmaN); RcppExport SEXP _DeMixT_Gfunc_2D_C(SEXP alphaSEXP, SEXP xSEXP, SEXP pSEXP, SEXP SSEXP, SEXP GSEXP, SEXP ySEXP, SEXP MuNSEXP, SEXP SigmaNSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type p(pSEXP); Rcpp::traits::input_parameter< int >::type S(SSEXP); Rcpp::traits::input_parameter< int >::type G(GSEXP); Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); rcpp_result_gen = Rcpp::wrap(Gfunc_2D_C(alpha, x, p, S, G, y, MuN, SigmaN)); return rcpp_result_gen; END_RCPP } // GoldenLine_search_2D double GoldenLine_search_2D(double alpha, NumericVector x, NumericVector p, NumericMatrix y, NumericVector MuN, NumericVector SigmaN, int S, int G, double steps); RcppExport SEXP _DeMixT_GoldenLine_search_2D(SEXP alphaSEXP, SEXP xSEXP, SEXP pSEXP, SEXP ySEXP, SEXP MuNSEXP, SEXP SigmaNSEXP, SEXP SSEXP, SEXP GSEXP, SEXP stepsSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type p(pSEXP); Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< int >::type S(SSEXP); Rcpp::traits::input_parameter< int >::type G(GSEXP); Rcpp::traits::input_parameter< double >::type steps(stepsSEXP); rcpp_result_gen = Rcpp::wrap(GoldenLine_search_2D(alpha, x, p, y, MuN, SigmaN, S, G, steps)); return rcpp_result_gen; END_RCPP } // GoldenLine_search_Pi_2D double GoldenLine_search_Pi_2D(double alpha, NumericVector x, NumericVector p, NumericMatrix y, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, NumericVector SigmaT, double steps); RcppExport SEXP _DeMixT_GoldenLine_search_Pi_2D(SEXP alphaSEXP, SEXP xSEXP, SEXP pSEXP, SEXP ySEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP, SEXP stepsSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type p(pSEXP); Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); Rcpp::traits::input_parameter< double >::type steps(stepsSEXP); rcpp_result_gen = Rcpp::wrap(GoldenLine_search_Pi_2D(alpha, x, p, y, MuN, MuT, SigmaN, SigmaT, steps)); return rcpp_result_gen; END_RCPP } // GoldenLine_search_MuT_2D double GoldenLine_search_MuT_2D(double alpha, NumericVector x, NumericVector p, NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector SigmaN, NumericVector SigmaT, double steps); RcppExport SEXP _DeMixT_GoldenLine_search_MuT_2D(SEXP alphaSEXP, SEXP xSEXP, SEXP pSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP SigmaNSEXP, SEXP SigmaTSEXP, SEXP stepsSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type p(pSEXP); Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaT(SigmaTSEXP); Rcpp::traits::input_parameter< double >::type steps(stepsSEXP); rcpp_result_gen = Rcpp::wrap(GoldenLine_search_MuT_2D(alpha, x, p, y, Pi, MuN, SigmaN, SigmaT, steps)); return rcpp_result_gen; END_RCPP } // GoldenLine_search_SigmaT_2D double GoldenLine_search_SigmaT_2D(double alpha, NumericVector x, NumericVector p, NumericMatrix y, NumericVector Pi, NumericVector MuN, NumericVector MuT, NumericVector SigmaN, double steps); RcppExport SEXP _DeMixT_GoldenLine_search_SigmaT_2D(SEXP alphaSEXP, SEXP xSEXP, SEXP pSEXP, SEXP ySEXP, SEXP PiSEXP, SEXP MuNSEXP, SEXP MuTSEXP, SEXP SigmaNSEXP, SEXP stepsSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< NumericVector >::type p(pSEXP); Rcpp::traits::input_parameter< NumericMatrix >::type y(ySEXP); Rcpp::traits::input_parameter< NumericVector >::type Pi(PiSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuN(MuNSEXP); Rcpp::traits::input_parameter< NumericVector >::type MuT(MuTSEXP); Rcpp::traits::input_parameter< NumericVector >::type SigmaN(SigmaNSEXP); Rcpp::traits::input_parameter< double >::type steps(stepsSEXP); rcpp_result_gen = Rcpp::wrap(GoldenLine_search_SigmaT_2D(alpha, x, p, y, Pi, MuN, MuT, SigmaN, steps)); return rcpp_result_gen; END_RCPP } DeMixT/src/init.c0000644000175200017520000003470214710233064014632 0ustar00biocbuildbiocbuild#include #include #include // for NULL #include /* .Call calls */ /* .Call calls */ extern SEXP _DeMixT_Alpha_search_2D(SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_Alpha_search_2D_sigma(SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_Alpha_search_MuT_2D(SEXP, SEXP, SEXP); extern SEXP _DeMixT_Alpha_search_Pi_2D(SEXP, SEXP, SEXP); extern SEXP _DeMixT_Alpha_search_SigmaT_2D(SEXP, SEXP, SEXP); extern SEXP _DeMixT_D1f0MuT_func_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D1f0Pi_func_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D1f0SigmaT_func_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D1Loglikelihood_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D1Loglikelihood_log_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D1MuT_Loglikelihood_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D1MuTSigmaT_Loglikelihood_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D1Pi_Loglikelihood_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D1SigmaT_Loglikelihood_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2f0MuT_func_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2f0MuTSigmaT_func_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2f0Pi_func_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2f0PiMuT_func_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2f0PiSigmaT_func_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2f0SigmaT_func_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2Loglikelihood_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2Loglikelihood_log_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2Loglikelihood_unit_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2MuT_inner_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2MuT_outer_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2MuTSigmaT_inner_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2MuTSigmaT_outer_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2Pi_inner_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2Pi_outer_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2PiMuT_inner_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2PiMuT_outer_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2PiSigmaT_inner_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2PiSigmaT_outer_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2SigmaT_inner_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_D2SigmaT_outer_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_DMuT_inner_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_DMuT_outer_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_DPi_inner_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_DPi_outer_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_DSigmaT_inner_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_DSigmaT_outer_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_f0_func_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_ft_y(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_Gfunc_2D_C(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_GoldenLine_search_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_GoldenLine_search_MuT_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_GoldenLine_search_Pi_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_GoldenLine_search_SigmaT_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_GoldenSection_Loglikelihood_MuT_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_GoldenSection_Loglikelihood_Pi_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_GoldenSection_Loglikelihood_SigmaT_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_Gt(SEXP, SEXP, SEXP); extern SEXP _DeMixT_Gt_vec(SEXP, SEXP, SEXP); extern SEXP _DeMixT_inner_trapez_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_innerCPP_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_log_divide(SEXP, SEXP); extern SEXP _DeMixT_Loglikelihood_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_Loglikelihood_2D_L1(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_Loglikelihood_ft_y(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_Loglikelihood_MuT_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_Loglikelihood_Pi_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_Loglikelihood_SigmaT_2D(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_mint(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_pf_y(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_pmin_y(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_SoftThreshold(SEXP, SEXP); extern SEXP _DeMixT_SoftThreshold_vec(SEXP, SEXP); extern SEXP _DeMixT_tf_y(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_tmin_y(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_tmin_y2(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_Unit1(SEXP, SEXP, SEXP); extern SEXP _DeMixT_x_update_2D(SEXP, SEXP, SEXP, SEXP, SEXP); extern SEXP _DeMixT_x_update_inv_2D(SEXP, SEXP, SEXP); static const R_CallMethodDef CallEntries[] = { {"_DeMixT_Alpha_search_2D", (DL_FUNC) &_DeMixT_Alpha_search_2D, 4}, {"_DeMixT_Alpha_search_2D_sigma", (DL_FUNC) &_DeMixT_Alpha_search_2D_sigma, 4}, {"_DeMixT_Alpha_search_MuT_2D", (DL_FUNC) &_DeMixT_Alpha_search_MuT_2D, 3}, {"_DeMixT_Alpha_search_Pi_2D", (DL_FUNC) &_DeMixT_Alpha_search_Pi_2D, 3}, {"_DeMixT_Alpha_search_SigmaT_2D", (DL_FUNC) &_DeMixT_Alpha_search_SigmaT_2D, 3}, {"_DeMixT_D1f0MuT_func_2D", (DL_FUNC) &_DeMixT_D1f0MuT_func_2D, 6}, {"_DeMixT_D1f0Pi_func_2D", (DL_FUNC) &_DeMixT_D1f0Pi_func_2D, 6}, {"_DeMixT_D1f0SigmaT_func_2D", (DL_FUNC) &_DeMixT_D1f0SigmaT_func_2D, 6}, {"_DeMixT_D1Loglikelihood_2D", (DL_FUNC) &_DeMixT_D1Loglikelihood_2D, 6}, {"_DeMixT_D1Loglikelihood_log_2D", (DL_FUNC) &_DeMixT_D1Loglikelihood_log_2D, 6}, {"_DeMixT_D1MuT_Loglikelihood_2D", (DL_FUNC) &_DeMixT_D1MuT_Loglikelihood_2D, 6}, {"_DeMixT_D1MuTSigmaT_Loglikelihood_2D", (DL_FUNC) &_DeMixT_D1MuTSigmaT_Loglikelihood_2D, 6}, {"_DeMixT_D1Pi_Loglikelihood_2D", (DL_FUNC) &_DeMixT_D1Pi_Loglikelihood_2D, 6}, {"_DeMixT_D1SigmaT_Loglikelihood_2D", (DL_FUNC) &_DeMixT_D1SigmaT_Loglikelihood_2D, 6}, {"_DeMixT_D2f0MuT_func_2D", (DL_FUNC) &_DeMixT_D2f0MuT_func_2D, 6}, {"_DeMixT_D2f0MuTSigmaT_func_2D", (DL_FUNC) &_DeMixT_D2f0MuTSigmaT_func_2D, 6}, {"_DeMixT_D2f0Pi_func_2D", (DL_FUNC) &_DeMixT_D2f0Pi_func_2D, 6}, {"_DeMixT_D2f0PiMuT_func_2D", (DL_FUNC) &_DeMixT_D2f0PiMuT_func_2D, 6}, {"_DeMixT_D2f0PiSigmaT_func_2D", (DL_FUNC) &_DeMixT_D2f0PiSigmaT_func_2D, 6}, {"_DeMixT_D2f0SigmaT_func_2D", (DL_FUNC) &_DeMixT_D2f0SigmaT_func_2D, 6}, {"_DeMixT_D2Loglikelihood_2D", (DL_FUNC) &_DeMixT_D2Loglikelihood_2D, 6}, {"_DeMixT_D2Loglikelihood_log_2D", (DL_FUNC) &_DeMixT_D2Loglikelihood_log_2D, 6}, {"_DeMixT_D2Loglikelihood_unit_2D", (DL_FUNC) &_DeMixT_D2Loglikelihood_unit_2D, 6}, {"_DeMixT_D2MuT_inner_2D", (DL_FUNC) &_DeMixT_D2MuT_inner_2D, 7}, {"_DeMixT_D2MuT_outer_2D", (DL_FUNC) &_DeMixT_D2MuT_outer_2D, 6}, {"_DeMixT_D2MuTSigmaT_inner_2D", (DL_FUNC) &_DeMixT_D2MuTSigmaT_inner_2D, 7}, {"_DeMixT_D2MuTSigmaT_outer_2D", (DL_FUNC) &_DeMixT_D2MuTSigmaT_outer_2D, 6}, {"_DeMixT_D2Pi_inner_2D", (DL_FUNC) &_DeMixT_D2Pi_inner_2D, 8}, {"_DeMixT_D2Pi_outer_2D", (DL_FUNC) &_DeMixT_D2Pi_outer_2D, 6}, {"_DeMixT_D2PiMuT_inner_2D", (DL_FUNC) &_DeMixT_D2PiMuT_inner_2D, 7}, {"_DeMixT_D2PiMuT_outer_2D", (DL_FUNC) &_DeMixT_D2PiMuT_outer_2D, 6}, {"_DeMixT_D2PiSigmaT_inner_2D", (DL_FUNC) &_DeMixT_D2PiSigmaT_inner_2D, 7}, {"_DeMixT_D2PiSigmaT_outer_2D", (DL_FUNC) &_DeMixT_D2PiSigmaT_outer_2D, 6}, {"_DeMixT_D2SigmaT_inner_2D", (DL_FUNC) &_DeMixT_D2SigmaT_inner_2D, 7}, {"_DeMixT_D2SigmaT_outer_2D", (DL_FUNC) &_DeMixT_D2SigmaT_outer_2D, 6}, {"_DeMixT_DMuT_inner_2D", (DL_FUNC) &_DeMixT_DMuT_inner_2D, 7}, {"_DeMixT_DMuT_outer_2D", (DL_FUNC) &_DeMixT_DMuT_outer_2D, 6}, {"_DeMixT_DPi_inner_2D", (DL_FUNC) &_DeMixT_DPi_inner_2D, 7}, {"_DeMixT_DPi_outer_2D", (DL_FUNC) &_DeMixT_DPi_outer_2D, 6}, {"_DeMixT_DSigmaT_inner_2D", (DL_FUNC) &_DeMixT_DSigmaT_inner_2D, 7}, {"_DeMixT_DSigmaT_outer_2D", (DL_FUNC) &_DeMixT_DSigmaT_outer_2D, 6}, {"_DeMixT_f0_func_2D", (DL_FUNC) &_DeMixT_f0_func_2D, 6}, {"_DeMixT_ft_y", (DL_FUNC) &_DeMixT_ft_y, 7}, {"_DeMixT_Gfunc_2D_C", (DL_FUNC) &_DeMixT_Gfunc_2D_C, 8}, {"_DeMixT_GoldenLine_search_2D", (DL_FUNC) &_DeMixT_GoldenLine_search_2D, 9}, {"_DeMixT_GoldenLine_search_MuT_2D", (DL_FUNC) &_DeMixT_GoldenLine_search_MuT_2D, 9}, {"_DeMixT_GoldenLine_search_Pi_2D", (DL_FUNC) &_DeMixT_GoldenLine_search_Pi_2D, 9}, {"_DeMixT_GoldenLine_search_SigmaT_2D", (DL_FUNC) &_DeMixT_GoldenLine_search_SigmaT_2D, 9}, {"_DeMixT_GoldenSection_Loglikelihood_MuT_2D", (DL_FUNC) &_DeMixT_GoldenSection_Loglikelihood_MuT_2D, 6}, {"_DeMixT_GoldenSection_Loglikelihood_Pi_2D", (DL_FUNC) &_DeMixT_GoldenSection_Loglikelihood_Pi_2D, 6}, {"_DeMixT_GoldenSection_Loglikelihood_SigmaT_2D", (DL_FUNC) &_DeMixT_GoldenSection_Loglikelihood_SigmaT_2D, 6}, {"_DeMixT_Gt", (DL_FUNC) &_DeMixT_Gt, 3}, {"_DeMixT_Gt_vec", (DL_FUNC) &_DeMixT_Gt_vec, 3}, {"_DeMixT_inner_trapez_2D", (DL_FUNC) &_DeMixT_inner_trapez_2D, 6}, {"_DeMixT_innerCPP_2D", (DL_FUNC) &_DeMixT_innerCPP_2D, 7}, {"_DeMixT_log_divide", (DL_FUNC) &_DeMixT_log_divide, 2}, {"_DeMixT_Loglikelihood_2D", (DL_FUNC) &_DeMixT_Loglikelihood_2D, 6}, {"_DeMixT_Loglikelihood_2D_L1", (DL_FUNC) &_DeMixT_Loglikelihood_2D_L1, 7}, {"_DeMixT_Loglikelihood_ft_y", (DL_FUNC) &_DeMixT_Loglikelihood_ft_y, 6}, {"_DeMixT_Loglikelihood_MuT_2D", (DL_FUNC) &_DeMixT_Loglikelihood_MuT_2D, 7}, {"_DeMixT_Loglikelihood_Pi_2D", (DL_FUNC) &_DeMixT_Loglikelihood_Pi_2D, 7}, {"_DeMixT_Loglikelihood_SigmaT_2D", (DL_FUNC) &_DeMixT_Loglikelihood_SigmaT_2D, 7}, {"_DeMixT_mint", (DL_FUNC) &_DeMixT_mint, 6}, {"_DeMixT_pf_y", (DL_FUNC) &_DeMixT_pf_y, 7}, {"_DeMixT_pmin_y", (DL_FUNC) &_DeMixT_pmin_y, 9}, {"_DeMixT_SoftThreshold", (DL_FUNC) &_DeMixT_SoftThreshold, 2}, {"_DeMixT_SoftThreshold_vec", (DL_FUNC) &_DeMixT_SoftThreshold_vec, 2}, {"_DeMixT_tf_y", (DL_FUNC) &_DeMixT_tf_y, 7}, {"_DeMixT_tmin_y", (DL_FUNC) &_DeMixT_tmin_y, 8}, {"_DeMixT_tmin_y2", (DL_FUNC) &_DeMixT_tmin_y2, 9}, {"_DeMixT_Unit1", (DL_FUNC) &_DeMixT_Unit1, 3}, {"_DeMixT_x_update_2D", (DL_FUNC) &_DeMixT_x_update_2D, 5}, {"_DeMixT_x_update_inv_2D", (DL_FUNC) &_DeMixT_x_update_inv_2D, 3}, {NULL, NULL, 0} }; /* FIXME: Check these declarations against the C/Fortran source code. */ /* .C calls */ extern void checkopenmp(void *); extern void Tdemix(void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *); static const R_CMethodDef CEntries[] = { {"checkopenmp", (DL_FUNC) &checkopenmp, 1}, {"Tdemix", (DL_FUNC) &Tdemix, 27}, {NULL, NULL, 0} }; void R_init_DeMixT(DllInfo *dll) { R_registerRoutines(dll, CEntries, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } DeMixT/src/registerDynamicSymbol.c0000644000175200017520000000034514710233064020202 0ustar00biocbuildbiocbuild// RegisteringDynamic Symbols #include #include #include void R_init_markovchain(DllInfo* info) { R_registerRoutines(info, NULL, NULL, NULL, NULL); R_useDynamicSymbols(info, TRUE); } DeMixT/vignettes/0000755000175200017520000000000014710276060014741 5ustar00biocbuildbiocbuildDeMixT/vignettes/Algorithm.png0000644000175200017520000042732514710233064017407 0ustar00biocbuildbiocbuildPNG  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